NMTC and LIHTC Evaluation

Library

library(here)         # relative filepaths for reproducibility
library(rio)          # read excel file from URL
library(tidyverse)    # data wrangling
library(stringi)      # string data wrangling
library(tidycensus)   # US census data
library(ggplot2)      # data visualization
library(kableExtra)   # table formatting
library(scales)       # palette and number formatting
library(unhcrthemes)  # data visualization themes
library(ggrepel)      # data visualization formatting to avoid overlapping
library(rcompanion)   # data visualization of variable distribution
library(ggpubr)       # data visualization of variable distribution
library(moments)      # measures of skewness and kurtosis
library(tinytable)    # format regression tables
library(modelsummary) # format regression tables

import::here( "fips_census_regions",
              "load_svi_data",
              "merge_svi_data",
              "census_division",
              "slopegraph_plot",
              "census_pull",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_dodson.R"),
             .character_only = TRUE)

# Load API key, assign to TidyCensus Package
source(here::here("analysis/password.R"))
census_api_key(census_api_key)

Data

# Load NMTC AND LIHTC data sets

svi_divisional_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_nmtc.rds")))

svi_national_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_nmtc.rds")))

svi_divisional_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_lihtc.rds")))

svi_national_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_lihtc.rds")))

House Price Index Data

hpi_df <- read.csv("https://r-class.github.io/paf-515-course-materials/data/raw/HPI/HPI_AT_BDL_tract.csv")

hpi_df_10_20 <- hpi_df %>% 
  mutate(GEOID10 = str_pad(tract, 11, "left", pad=0)) %>% 
  filter(year %in% c(2010, 2020))  %>%
 select(GEOID10, state_abbr, year, hpi) %>%
  pivot_wider(names_from = year, values_from = hpi) %>%
  mutate(housing_price_index10 = `2010`,
         housing_price_index20 = `2020`) %>%
  select(GEOID10, state_abbr, housing_price_index10, housing_price_index20)

# View data
hpi_df_10_20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	state_abbr	housing_price_index10	housing_price_index20
01001020100	AL	132.35	152.78
01001020200	AL	123.78	123.37
01001020300	AL	158.57	167.01
01001020400	AL	165.11	179.60
01001020501	AL	172.55	180.96
01001020502	AL	158.75	164.25

# Drop state_abbr column for joining
hpi_df_10_20 <- hpi_df_10_20 %>% select(-state_abbr)

CBSA Crosswalk

msa_csa_crosswalk <- rio::import("https://r-class.github.io/paf-515-course-materials/data/raw/CSA_MSA_Crosswalk/qcew-county-msa-csa-crosswalk.xlsx", which=4)

msa_csa_crosswalk <- msa_csa_crosswalk %>% 
  mutate(county_fips = str_pad(`County Code`, 5, "left", pad=0),
         cbsa = coalesce(`CSA Title`, `MSA Title`),
         cbsa_code = coalesce(`CSA Code`, `MSA Code`),
         county_title = `County Title`)  %>% 
  select(county_fips, county_title, cbsa, cbsa_code)

msa_csa_crosswalk %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_fips	county_title	cbsa	cbsa_code
01001	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01003	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01005	Barbour County, Alabama	Eufaula, AL-GA MicroSA	C2164
01007	Bibb County, Alabama	Birmingham-Hoover-Cullman, AL CSA	CS142
01009	Blount County, Alabama	Birmingham-Hoover-Cullman, AL CSA	CS142
01015	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150

Census Data

states <- list(svi_national_nmtc$state %>% unique())
states 

## [[1]]
##  [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "DC" "FL" "GA" "HI" "ID" "IL" "IN"
## [16] "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH"
## [31] "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT"
## [46] "VT" "VA" "WA" "WV" "WI" "WY"

census_pull10 <- lapply(states, census_pull, yr = 2010)

census_pull10_df <- census_pull10[[1]] %>%  
  # Drop margin of error column
  select(-moe) %>%
  # Add suffix to variable names
  mutate(variable = paste0(variable, "_10")) %>%
  # Pivot data frame
  pivot_wider(
    names_from = variable,
    values_from = c(estimate)
  )

census_pull10_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_10	Median_Home_Value_10
01001020100	Census Tract 201, Autauga County, Alabama	31769	120700
01001020200	Census Tract 202, Autauga County, Alabama	19437	138500
01001020300	Census Tract 203, Autauga County, Alabama	24146	111300
01001020400	Census Tract 204, Autauga County, Alabama	27735	126300
01001020500	Census Tract 205, Autauga County, Alabama	35517	173000
01001020600	Census Tract 206, Autauga County, Alabama	24597	110700
01001020700	Census Tract 207, Autauga County, Alabama	22114	93800
01001020801	Census Tract 208.01, Autauga County, Alabama	30841	258000
01001020802	Census Tract 208.02, Autauga County, Alabama	29006	145100
01001020900	Census Tract 209, Autauga County, Alabama	24841	108000

census_pull19 <- lapply(states, census_pull, yr = 2019)

census_pull19_df <- census_pull19[[1]] %>% 
  # Select columns
  select(GEOID, NAME, variable, estimate, moe) %>% 
  # Create individual FIPS columns for state, county, and tract
  mutate(FIPS_st = substr(GEOID, 1, 2),
         FIPS_county = substr(GEOID, 3, 5),
         FIPS_tract = substr(GEOID, 6, 11)) %>%
# Los Angeles, CA Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "037" & FIPS_st == "06" & FIPS_tract == "137000"), "930401", FIPS_tract )) %>%
# Pima County, AZ Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002704"), "002701", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002906"), "002903", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004118"), "410501", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004121"), "410502", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004125"), "410503", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005200"), "470400", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005300"), "470500", FIPS_tract2 )) %>%
# Madison County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030101"), "940101", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030102"), "940102", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030103"), "940103", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030200"), "940200", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030300"), "940300", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030401"), "940401", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030403"), "940403", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030600"), "940600", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030402"), "940700", FIPS_tract2 )) %>%
# Oneida County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024800"), "940000", FIPS_tract2 )) %>% 
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024700"), "940100", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024900"), "940200", FIPS_tract2 )) %>%  
                      # Move columns in data set
                      relocate(c(FIPS_st, FIPS_county, FIPS_tract, FIPS_tract2),.after = GEOID) %>%
                      # Create new GEOID column
                      mutate(GEOID = paste0(FIPS_st, FIPS_county, FIPS_tract2)) %>% 
                      # Drop newly created FIPS columns and margin of error
                      select(-FIPS_st, -FIPS_county, -FIPS_tract, -FIPS_tract2, -moe) %>% 
                      # Add suffix
                      mutate(variable = paste0(variable, "_19")) %>%
                      # Pivot data set
                      pivot_wider(
                        names_from = variable,
                        values_from = c(estimate)
                      ) 

census_pull19_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_19	Median_Home_Value_19
01001020100	Census Tract 201, Autauga County, Alabama	25970	136100
01001020200	Census Tract 202, Autauga County, Alabama	20154	90500
01001020300	Census Tract 203, Autauga County, Alabama	27383	122600
01001020400	Census Tract 204, Autauga County, Alabama	34620	152700
01001020500	Census Tract 205, Autauga County, Alabama	41178	186900
01001020600	Census Tract 206, Autauga County, Alabama	21146	103600
01001020700	Census Tract 207, Autauga County, Alabama	20934	82400
01001020801	Census Tract 208.01, Autauga County, Alabama	31667	322900
01001020802	Census Tract 208.02, Autauga County, Alabama	33086	171500
01001020900	Census Tract 209, Autauga County, Alabama	32677	156900

inflation_adj = 1.16

# Join 2010 and 2019 Median Income and Home Value Data
census_pull_df <- left_join(census_pull10_df, census_pull19_df[c("GEOID", "Median_Income_19", "Median_Home_Value_19")], join_by("GEOID" == "GEOID"))

# Create new inflation adjusted columns for 2010 median income and median home value, find changes over time
census_pull_df <- census_pull_df %>% 
                   mutate(Median_Income_10adj = Median_Income_10*inflation_adj,
                          Median_Home_Value_10adj = Median_Home_Value_10*inflation_adj,
                          Median_Income_Change = Median_Income_19 - Median_Income_10adj,
                          Median_Income_Change_pct = (Median_Income_19 - Median_Income_10adj)/Median_Income_10adj,
                          Median_Home_Value_Change = Median_Home_Value_19 - Median_Home_Value_10adj,
                          Median_Home_Value_Change_pct = (Median_Home_Value_19 - Median_Home_Value_10adj)/Median_Home_Value_10adj)

# View data
census_pull_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct
01001020100	Census Tract 201, Autauga County, Alabama	31769	120700	25970	136100	36852.04	140012	-10882.04	-0.2952900	-3912	-0.0279405
01001020200	Census Tract 202, Autauga County, Alabama	19437	138500	20154	90500	22546.92	160660	-2392.92	-0.1061307	-70160	-0.4366986
01001020300	Census Tract 203, Autauga County, Alabama	24146	111300	27383	122600	28009.36	129108	-626.36	-0.0223625	-6508	-0.0504074
01001020400	Census Tract 204, Autauga County, Alabama	27735	126300	34620	152700	32172.60	146508	2447.40	0.0760709	6192	0.0422639
01001020500	Census Tract 205, Autauga County, Alabama	35517	173000	41178	186900	41199.72	200680	-21.72	-0.0005272	-13780	-0.0686665
01001020600	Census Tract 206, Autauga County, Alabama	24597	110700	21146	103600	28532.52	128412	-7386.52	-0.2588807	-24812	-0.1932218
01001020700	Census Tract 207, Autauga County, Alabama	22114	93800	20934	82400	25652.24	108808	-4718.24	-0.1839309	-26408	-0.2427027
01001020801	Census Tract 208.01, Autauga County, Alabama	30841	258000	31667	322900	35775.56	299280	-4108.56	-0.1148426	23620	0.0789227
01001020802	Census Tract 208.02, Autauga County, Alabama	29006	145100	33086	171500	33646.96	168316	-560.96	-0.0166719	3184	0.0189168
01001020900	Census Tract 209, Autauga County, Alabama	24841	108000	32677	156900	28815.56	125280	3861.44	0.1340054	31620	0.2523946

NMTC Data

svi_divisional_nmtc_df0 <- left_join(svi_divisional_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_nmtc_df1 <- left_join(svi_divisional_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_nmtc_df <- left_join(svi_divisional_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_dollars_formatted	post10_nmtc_dollars_formatted	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
10001040201	10001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.45810	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.8543689	0.5386	0	435	5283	8.233958	0.19610	454	8.717358	0.256100	0	1588	30.491551	0.8927	1	537	3716	14.451023	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.427094	0.5240	0	1881	5208	36.117511	0.56890	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.1558872	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.0000000	0.3814	0	1.88140	0.3053	0	3.003500	0.7667	2	0.56890	0.56140	0	2.8296	0.6516	0	8.283400	0.5452	2	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.638513	0.280400	0	235	548	42.88321	0.46090	0	432	1732	24.94226	0.36220	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.21160	0	549	11.509434	0.23290	1352	28.343816	0.88650	1	490	3418.125	14.335346	0.4309	0	328	1263.2064	25.965669	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.7156348	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.0000000	0.2111	0	1.72140	0.2531	0	2.46127	0.45940	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2	Yes	\$0	\$0	Census Tract 402.01, Kent County, Delaware	29025	209100	32593	191200	33669.00	242556	-1076.00	-0.0319582	-51356	-0.2117284	172.23	224.88	Kent County, Delaware	Dover, DE MSA	C2010
10001040502	10001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.78190	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.3699871	0.6484	0	119	2121	5.610561	0.10710	518	24.820316	0.906800	1	480	22.999521	0.4910	0	328	1527	21.480026	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.005005	0.7960	1	560	2087	26.832774	0.45240	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.0000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.0000000	0.3814	0	1.74980	0.2670	0	3.766600	0.9666	4	0.45240	0.44640	0	1.9115	0.2071	1	7.880300	0.4785	5	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.839363	0.486500	0	168	262	64.12214	0.88940	1	312	953	32.73872	0.62590	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.40160	0	519	20.313112	0.68510	664	25.988258	0.79390	1	341	1854.295	18.389741	0.6427	0	195	614.6519	31.725274	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.5048544	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.0000000	0.2111	0	3.02120	0.6565	1	3.73230	0.96800	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5	Yes	\$0	\$0	Census Tract 405.02, Kent County, Delaware	31789	168400	31074	157700	36875.24	195344	-5801.24	-0.1573207	-37644	-0.1927062	143.93	162.18	Kent County, Delaware	Dover, DE MSA	C2010
10001040900	10001	040900	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2363	1205	1007	526	1741	30.212522	0.7006	0	171	1089	15.702479	0.9032	1	98	362	27.07182	0.37770	0	258	645	40.00000	0.36590	0	356	1007	35.35253	0.5041	0	248	1416	17.5141243	0.6235	0	118	2479	4.759984	0.08169	509	21.540415	0.863300	1	168	7.109606	0.0386	0	428	1427	29.992992	0.9611	1	44	387	11.36951	0.3400	0	50	2349	2.128565	0.6157	0	727	2363	30.765975	0.50480	0	1205	378	31.369295	0.8688	1	0	0.000000	0.1809	0	0	1007	0.0000000	0.1488	0	256	1007	25.422046	0.9457	1	622	2363	26.3224714	0.9741	1	2.81309	0.5851	1	2.818700	0.6717	2	0.50480	0.49810	0	3.1183	0.7840	3	9.254890	0.6833	6	2373	1114	1028	574	1679	34.18702	0.7904	1	19	1034	1.837524	0.1211	0	26	313	8.306709	0.029920	0	335	715	46.85315	0.55400	0	361	1028	35.11673	0.69080	0	224	1292	17.337461	0.7807	1	78	2250	3.466667	0.13250	0	501	21.112516	0.71690	208	8.765276	0.05851	0	391	1505.000	25.980066	0.9018	1	29	220.0000	13.181818	0.4476	0	7	2268	0.3086420	0.28740	0	974	2373.000	41.04509	0.5571	0	1114	476	42.7289048	0.9104	1	0	0.0000000	0.1800	0	5	1028	0.4863813	0.2927	0	248	1028.0000	24.124514	0.9466	1	678	2373	28.5714286	0.9778	1	2.51550	0.4976	2	2.41221	0.42860	1	0.5571	0.5503	0	3.3075	0.8411	3	8.79231	0.6264	6	Yes	\$0	\$0	Census Tract 409, Kent County, Delaware	13641	218000	13618	240500	15823.56	252880	-2205.56	-0.1393846	-12380	-0.0489560	199.51	191.02	Kent County, Delaware	Dover, DE MSA	C2010
10001041000	10001	041000	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5577	2570	2369	1291	5577	23.148646	0.5435	0	144	2702	5.329386	0.2938	0	468	1312	35.67073	0.67500	0	135	1057	12.77200	0.04274	0	603	2369	25.45378	0.1769	0	759	3504	21.6609589	0.7311	0	655	5999	10.918486	0.29800	594	10.650888	0.371200	0	1487	26.663080	0.7247	0	683	4587	14.889906	0.4947	0	364	1466	24.82947	0.7646	1	188	5105	3.682664	0.7342	0	3384	5577	60.677784	0.77850	1	2570	479	18.638132	0.7741	1	567	22.062257	0.8159	1	91	2369	3.8412832	0.8132	1	221	2369	9.328831	0.7266	0	9	5577	0.1613771	0.7632	1	2.04330	0.3511	0	3.089400	0.8041	1	0.77850	0.76820	1	3.8930	0.9705	4	9.804200	0.7522	6	6719	3107	2804	2006	6656	30.13822	0.7207	0	436	3058	14.257685	0.9556	1	299	1387	21.557318	0.521400	0	583	1417	41.14326	0.42160	0	882	2804	31.45506	0.58610	0	953	4915	19.389624	0.8333	1	509	6603	7.708617	0.39290	0	1221	18.172347	0.58640	1389	20.672719	0.47220	0	1393	5214.000	26.716532	0.9150	1	661	1752.0000	37.728310	0.9336	1	340	6411	5.3033848	0.82180	1	4068	6719.048	60.54429	0.7409	0	3107	469	15.0949469	0.7136	0	586	18.8606373	0.8099	1	54	2804	1.9258203	0.5840	0	253	2804.0000	9.022825	0.7342	0	70	6719	1.0418217	0.7345	0	3.48860	0.7870	2	3.72900	0.96760	3	0.7409	0.7319	0	3.5762	0.9178	1	11.53470	0.9313	6	Yes	\$0	\$0	Census Tract 410, Kent County, Delaware	32000	147800	30625	135400	37120.00	171448	-6495.00	-0.1749731	-36048	-0.2102562	168.61	200.50	Kent County, Delaware	Dover, DE MSA	C2010
10001041100	10001	041100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2957	800	738	499	2555	19.530333	0.4511	0	44	845	5.207101	0.2813	0	0	8	0.00000	0.00257	0	395	730	54.10959	0.69280	0	395	738	53.52304	0.9155	1	11	1118	0.9838998	0.0213	0	65	2559	2.540055	0.02694	0	0.000000	0.003549	0	1198	40.514035	0.9944	1	117	1192	9.815436	0.2221	0	133	693	19.19192	0.6322	0	42	2551	1.646413	0.5567	0	720	2957	24.349002	0.41770	0	800	0	0.000000	0.1428	0	0	0.000000	0.1809	0	0	738	0.0000000	0.1488	0	10	738	1.355014	0.1640	0	402	2957	13.5948597	0.9492	1	1.69614	0.2527	1	2.408949	0.4377	1	0.41770	0.41220	0	1.5857	0.1097	1	6.108489	0.2207	3	3881	1350	1322	1031	3618	28.49641	0.6874	0	58	877	6.613455	0.6891	0	0	3	0.000000	0.002567	0	758	1319	57.46778	0.78900	1	758	1322	57.33737	0.97720	1	41	1801	2.276513	0.0857	0	33	2762	1.194786	0.02847	0	64	1.649059	0.01027	1291	33.264623	0.97230	1	129	1470.497	8.772546	0.1457	0	113	1156.7160	9.769036	0.3067	0	4	3373	0.1185888	0.22310	0	1672	3881.437	43.07683	0.5797	0	1350	9	0.6666667	0.3180	0	10	0.7407407	0.4343	0	1	1322	0.0756430	0.2359	0	18	1321.8616	1.361716	0.1744	0	263	3881	6.7766040	0.9251	1	2.46787	0.4818	1	1.65807	0.08121	1	0.5797	0.5726	0	2.0877	0.2866	1	6.79334	0.3059	3	Yes	\$0	\$0	Census Tract 411, Kent County, Delaware	23029	NA	27357	NA	26713.64	NA	643.36	0.0240836	NA	NA	NA	NA	Kent County, Delaware	Dover, DE MSA	C2010
10001041200	10001	041200	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4723	1880	1742	778	4710	16.518047	0.3700	0	193	2264	8.524735	0.5848	0	481	1168	41.18151	0.80360	1	257	574	44.77352	0.47360	0	738	1742	42.36510	0.7087	0	573	3071	18.6584175	0.6563	0	471	3937	11.963424	0.34310	600	12.703790	0.504200	0	1257	26.614440	0.7223	0	583	3036	19.202899	0.7085	0	240	1183	20.28740	0.6645	0	190	4378	4.339881	0.7670	1	2933	4723	62.100360	0.78710	1	1880	327	17.393617	0.7606	1	353	18.776596	0.7841	1	41	1742	2.3536165	0.6746	0	92	1742	5.281286	0.5288	0	0	4723	0.0000000	0.3814	0	2.66290	0.5376	0	3.366500	0.8969	1	0.78710	0.77670	1	3.1295	0.7889	2	9.946000	0.7681	4	4135	1851	1712	870	4076	21.34446	0.5206	0	180	1879	9.579564	0.8567	1	384	1230	31.219512	0.844800	1	226	482	46.88797	0.55500	0	610	1712	35.63084	0.70270	0	286	2785	10.269300	0.5146	0	204	4124	4.946654	0.22200	0	755	18.258767	0.59080	1067	25.804111	0.78410	1	571	3057.653	18.674456	0.6593	0	375	1138.2043	32.946633	0.8989	1	26	3953	0.6577283	0.38940	0	2299	4134.641	55.60337	0.6982	0	1851	175	9.4543490	0.6291	0	438	23.6628849	0.8514	1	5	1712	0.2920561	0.2552	0	143	1712.2269	8.351697	0.7088	0	20	4135	0.4836759	0.6479	0	2.81660	0.5950	1	3.32250	0.89620	2	0.6982	0.6897	0	3.0924	0.7625	1	9.92970	0.7729	4	Yes	\$0	\$0	Census Tract 412, Kent County, Delaware	23257	177100	26589	261500	26978.12	205436	-389.12	-0.0144235	56064	0.2729025	165.96	190.13	Kent County, Delaware	Dover, DE MSA	C2010
10001041300	10001	041300	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	1912	1067	876	596	1880	31.702128	0.7303	0	101	790	12.784810	0.8216	1	124	361	34.34903	0.63440	0	169	515	32.81553	0.22600	0	293	876	33.44749	0.4394	0	76	1123	6.7675868	0.2300	0	172	1975	8.708861	0.21160	222	11.610879	0.432500	0	451	23.587866	0.5306	0	263	1415	18.586572	0.6820	0	189	504	37.50000	0.9172	1	0	1739	0.000000	0.1022	0	780	1912	40.794979	0.61730	0	1067	115	10.777882	0.6734	0	22	2.061856	0.5184	0	0	876	0.0000000	0.1488	0	139	876	15.867580	0.8735	1	0	1912	0.0000000	0.3814	0	2.43290	0.4667	1	2.664500	0.5849	1	0.61730	0.60910	0	2.5955	0.5310	1	8.310200	0.5491	3	2056	1010	883	839	2047	40.98681	0.8824	1	105	1049	10.009533	0.8718	1	84	319	26.332288	0.715100	0	282	564	50.00000	0.62780	0	366	883	41.44960	0.82570	1	99	1267	7.813733	0.3940	0	82	2048	4.003906	0.16480	0	341	16.585603	0.50570	574	27.918288	0.87170	1	322	1474.000	21.845319	0.7931	1	222	540.0000	41.111111	0.9533	1	37	1918	1.9290928	0.61360	0	1101	2056.000	53.55058	0.6819	0	1010	110	10.8910891	0.6527	0	0	0.0000000	0.1800	0	14	883	1.5855040	0.5304	0	106	883.0000	12.004530	0.8210	1	9	2056	0.4377432	0.6354	0	3.13870	0.6919	3	3.73740	0.96820	3	0.6819	0.6736	0	2.8195	0.6344	1	10.37750	0.8249	7	Yes	\$0	\$0	Census Tract 413, Kent County, Delaware	26161	170700	23750	162800	30346.76	198012	-6596.76	-0.2173794	-35212	-0.1778276	125.53	144.48	Kent County, Delaware	Dover, DE MSA	C2010
10001041400	10001	041400	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	3520	1746	1453	1040	3141	33.110474	0.7548	1	118	1256	9.394905	0.6456	0	94	467	20.12848	0.14600	0	648	986	65.72008	0.88750	1	742	1453	51.06676	0.8854	1	326	2060	15.8252427	0.5697	0	135	3008	4.488032	0.07466	475	13.494318	0.555300	0	917	26.051136	0.6887	0	572	2200	26.000000	0.9122	1	238	654	36.39144	0.9103	1	54	3062	1.763553	0.5730	0	2306	3520	65.511364	0.80580	1	1746	560	32.073310	0.8720	1	0	0.000000	0.1809	0	6	1453	0.4129387	0.3280	0	293	1453	20.165176	0.9164	1	287	3520	8.1534091	0.9253	1	2.93016	0.6234	2	3.639500	0.9508	2	0.80580	0.79520	1	3.2226	0.8240	3	10.598060	0.8394	8	3390	1833	1617	1009	3251	31.03660	0.7389	0	135	1853	7.285483	0.7386	0	205	592	34.628378	0.899300	1	727	1025	70.92683	0.95250	1	932	1617	57.63760	0.97780	1	255	2253	11.318242	0.5621	0	380	3194	11.897308	0.64010	0	336	9.911504	0.16120	665	19.616519	0.40580	0	525	2529.000	20.759193	0.7511	1	243	622.0000	39.067524	0.9428	1	30	3246	0.9242144	0.45190	0	2363	3390.000	69.70501	0.8011	1	1833	637	34.7517730	0.8775	1	0	0.0000000	0.1800	0	41	1617	2.5355597	0.6635	0	358	1617.0000	22.139765	0.9372	1	171	3390	5.0442478	0.9052	1	3.65750	0.8282	1	2.71280	0.61900	2	0.8011	0.7913	1	3.5634	0.9151	3	10.73480	0.8623	7	Yes	\$0	\$0	Census Tract 414, Kent County, Delaware	22105	195200	25352	190000	25641.80	226432	-289.80	-0.0113019	-36432	-0.1608960	174.43	222.67	Kent County, Delaware	Dover, DE MSA	C2010
10001041500	10001	041500	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4098	1661	1469	756	4098	18.448023	0.4222	0	141	2212	6.374322	0.3968	0	313	1109	28.22362	0.42170	0	204	360	56.66667	0.74570	0	517	1469	35.19401	0.4992	0	295	2750	10.7272727	0.3855	0	243	3946	6.158135	0.12390	626	15.275744	0.659400	0	998	24.353343	0.5834	0	545	3015	18.076285	0.6599	0	217	1085	20.00000	0.6563	0	113	3952	2.859312	0.6795	0	1972	4098	48.121035	0.68770	0	1661	209	12.582781	0.6992	0	10	0.602047	0.4063	0	0	1469	0.0000000	0.1488	0	52	1469	3.539823	0.3853	0	0	4098	0.0000000	0.3814	0	1.82760	0.2906	0	3.238500	0.8589	0	0.68770	0.67860	0	2.0210	0.2527	0	7.774800	0.4611	0	4506	1536	1516	1520	4501	33.77027	0.7838	1	145	2238	6.478999	0.6780	0	189	761	24.835742	0.660400	0	375	755	49.66887	0.61980	0	564	1516	37.20317	0.74010	0	193	2917	6.616387	0.3313	0	529	4479	11.810672	0.63540	0	574	12.738571	0.29900	1176	26.098535	0.79920	1	647	3301.887	19.594854	0.7040	0	339	1022.2089	33.163474	0.9011	1	62	4156	1.4918191	0.55350	0	2106	4506.129	46.73634	0.6179	0	1536	290	18.8802083	0.7598	1	0	0.0000000	0.1800	0	69	1516	4.5514512	0.8288	1	147	1516.4830	9.693482	0.7574	1	26	4506	0.5770084	0.6682	0	3.16860	0.7007	1	3.25680	0.87570	2	0.6179	0.6104	0	3.1942	0.8024	3	10.23750	0.8094	6	Yes	\$0	\$0	Census Tract 415, Kent County, Delaware	24801	177400	27014	171300	28769.16	205784	-1755.16	-0.0610084	-34484	-0.1675738	216.49	246.21	Kent County, Delaware	Dover, DE MSA	C2010
10001042000	10001	042000	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	3037	1200	1121	567	3037	18.669740	0.4287	0	178	1552	11.469072	0.7632	1	388	928	41.81034	0.81540	1	86	193	44.55959	0.46980	0	474	1121	42.28368	0.7069	0	508	2110	24.0758294	0.7927	1	462	3029	15.252559	0.49000	413	13.598946	0.562000	0	613	20.184393	0.3207	0	489	2427	20.148331	0.7477	0	117	845	13.84615	0.4393	0	32	2868	1.115760	0.4677	0	137	3037	4.511031	0.07128	0	1200	0	0.000000	0.1428	0	362	30.166667	0.8820	1	0	1121	0.0000000	0.1488	0	37	1121	3.300624	0.3644	0	0	3037	0.0000000	0.3814	0	3.18150	0.7026	2	2.537400	0.5126	0	0.07128	0.07033	0	1.9194	0.2102	1	7.709580	0.4513	3	3460	1291	1144	555	3460	16.04046	0.3745	0	128	1787	7.162843	0.7294	0	175	1052	16.634981	0.280300	0	19	92	20.65217	0.08512	0	194	1144	16.95804	0.09936	0	711	2398	29.649708	0.9637	1	711	3440	20.668605	0.91280	1	455	13.150289	0.32050	792	22.890173	0.61620	0	470	2648.000	17.749245	0.6107	0	53	872.0000	6.077982	0.1545	0	38	3260	1.1656442	0.50140	0	590	3460.000	17.05202	0.2337	0	1291	0	0.0000000	0.1260	0	295	22.8505035	0.8450	1	9	1144	0.7867133	0.3721	0	196	1144.0000	17.132867	0.8985	1	27	3460	0.7803468	0.7044	0	3.07976	0.6746	2	2.20330	0.30100	0	0.2337	0.2308	0	2.9460	0.6984	2	8.46276	0.5716	4	Yes	\$0	\$0	Census Tract 420, Kent County, Delaware	25586	173900	32641	194500	29679.76	201724	2961.24	0.0997730	-7224	-0.0358113	147.99	173.96	Kent County, Delaware	Dover, DE MSA	C2010

svi_national_nmtc_df0 <- left_join(svi_national_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_nmtc_df1 <- left_join(svi_national_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_nmtc_df <- left_join(svi_national_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_dollars_formatted	post10_nmtc_project_cnt	post10_nmtc_dollars	post10_nmtc_dollars_formatted	nmtc_flag	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
01001020200	01001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.57540	0	49	439	11.16173	0.02067	105	291	36.08247	0.30190	0	154	730	21.09589	0.09312	339	1265	26.798419	0.8392	1	313	2012	15.55666	0.6000	0	204	10.09901	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.8351	1	15	1890	0.7936508	0.40130	0	1243	2020	61.534653	0.77810	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.780822	0.5406	0	115	730	15.7534247	0.83820	1	0	2020	0.0000	0.3640	0	2.70312	0.5665	1	3.27660	0.8614	3	0.77810	0.7709	1	2.53160	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.413633	0.6427	0	29	717	4.044630	0.41320	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.40410	0	139	1313	10.586443	0.5601	0	91	1533	5.936073	0.4343	0	284	16.163916	0.5169	0	325	18.49744	0.28510	0	164	1208.000	13.576159	0.4127	0	42	359.0000	11.6991643	0.39980	0	0	1651	0.0000000	0.09479	1116	1757.000	63.5173591	0.759100	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.46880	0	57	573.000	9.947644	0.7317	212	1757	12.0660216	0.9549	1	2.45440	0.4888	0	1.70929	0.10250	0	0.759100	0.752700	1	2.91300	0.6862	1	7.835790	0.4802	2	Yes	\$0	0	0	\$0	0	Census Tract 202, Autauga County, Alabama	19437	138500	20154	90500	22546.92	160660	-2392.92	-0.1061307	-70160	-0.4366986	123.78	123.37	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01001020700	01001	020700	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2664	1254	1139	710	2664	26.65165	0.6328	0	29	1310	2.213741	0.05255	0	134	710	18.87324	0.13890	187	429	43.58974	0.47090	0	321	1139	28.18262	0.28130	396	1852	21.382289	0.7478	0	345	2878	11.98749	0.4459	0	389	14.60210	0.6417	0	599	22.48499	0.4007	0	510	2168	23.52399	0.8752	1	228	712	32.022472	0.8712	1	0	2480	0.0000000	0.09298	0	694	2664	26.051051	0.51380	0	1254	8	0.6379585	0.2931	0	460	36.6826156	0.9714	1	0	1139	0.000000	0.1238	0	125	1139	10.9745391	0.74770	0	0	2664	0.0000	0.3640	0	2.16035	0.4069	0	2.88178	0.6997	2	0.51380	0.5090	0	2.50000	0.4882	1	8.05593	0.5185	3	3562	1313	1248	1370	3528	38.832200	0.8512	1	128	1562	8.194622	0.79350	1	168	844	19.905213	0.44510	0	237	404	58.66337	0.8359	1	405	1248	32.45192	0.60420	0	396	2211	17.910448	0.7857	1	444	3547	12.517620	0.7758	1	355	9.966311	0.1800	0	954	26.78271	0.79230	1	629	2593.000	24.257617	0.8730	1	171	797.0000	21.4554580	0.71860	0	0	3211	0.0000000	0.09479	1009	3562.000	28.3267827	0.466800	0	1313	14	1.0662605	0.3165	0	443	33.7395278	0.9663	1	73	1248	5.8493590	0.82110	1	17	1248.000	1.362180	0.1554	112	3562	3.1443010	0.8514	1	3.81040	0.8569	4	2.65869	0.58470	2	0.466800	0.462900	0	3.11070	0.7714	3	10.046590	0.7851	9	Yes	\$0	0	0	\$0	0	Census Tract 207, Autauga County, Alabama	22114	93800	20934	82400	25652.24	108808	-4718.24	-0.1839309	-26408	-0.2427027	95.94	108.47	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01001021100	01001	021100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3298	1502	1323	860	3298	26.07641	0.6211	0	297	1605	18.504673	0.94340	1	250	1016	24.60630	0.32070	74	307	24.10423	0.11920	0	324	1323	24.48980	0.17380	710	2231	31.824294	0.8976	1	654	3565	18.34502	0.7018	0	411	12.46210	0.5001	0	738	22.37720	0.3934	0	936	2861	32.71583	0.9807	1	138	825	16.727273	0.5715	0	9	3155	0.2852615	0.25010	0	1979	3298	60.006064	0.77030	1	1502	14	0.9320905	0.3234	0	659	43.8748336	0.9849	1	44	1323	3.325775	0.7062	0	137	1323	10.3552532	0.73130	0	0	3298	0.0000	0.3640	0	3.33770	0.7351	2	2.69580	0.6028	1	0.77030	0.7631	1	3.10980	0.7827	1	9.91360	0.7557	5	3499	1825	1462	1760	3499	50.300086	0.9396	1	42	966	4.347826	0.45390	0	426	1274	33.437991	0.85200	1	52	188	27.65957	0.1824	0	478	1462	32.69494	0.61110	0	422	2488	16.961415	0.7638	1	497	3499	14.204058	0.8246	1	853	24.378394	0.8688	1	808	23.09231	0.58290	0	908	2691.100	33.740844	0.9808	1	179	811.6985	22.0525243	0.73230	0	8	3248	0.2463054	0.26220	1986	3498.713	56.7637257	0.717500	0	1825	29	1.5890411	0.3551	0	576	31.5616438	0.9594	1	88	1462	6.0191518	0.82690	1	148	1461.993	10.123166	0.7364	38	3499	1.0860246	0.7013	0	3.59300	0.8073	3	3.42700	0.91560	2	0.717500	0.711400	0	3.57910	0.9216	2	11.316600	0.9150	7	Yes	\$0	0	0	\$0	0	Census Tract 211, Autauga County, Alabama	17997	74000	20620	88600	20876.52	85840	-256.52	-0.0122875	2760	0.0321528	134.13	145.41	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01003010200	01003	010200	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	2612	1220	1074	338	2605	12.97505	0.2907	0	44	1193	3.688181	0.14720	0	172	928	18.53448	0.13090	31	146	21.23288	0.09299	0	203	1074	18.90130	0.05657	455	1872	24.305556	0.8016	1	456	2730	16.70330	0.6445	0	401	15.35222	0.6847	0	563	21.55436	0.3406	0	410	2038	20.11776	0.7755	1	64	779	8.215661	0.2181	0	0	2510	0.0000000	0.09298	0	329	2612	12.595712	0.31130	0	1220	38	3.1147541	0.4648	0	385	31.5573770	0.9545	1	20	1074	1.862197	0.5509	0	43	1074	4.0037244	0.40880	0	0	2612	0.0000	0.3640	0	1.94057	0.3398	1	2.11188	0.2802	1	0.31130	0.3084	0	2.74300	0.6129	1	7.10675	0.3771	3	2928	1312	1176	884	2928	30.191257	0.7334	0	29	1459	1.987663	0.13560	0	71	830	8.554217	0.03726	0	134	346	38.72832	0.3964	0	205	1176	17.43197	0.12010	0	294	2052	14.327485	0.6940	0	219	2925	7.487179	0.5423	0	556	18.989071	0.6705	0	699	23.87295	0.63390	0	489	2226.455	21.963167	0.8122	1	191	783.8820	24.3659136	0.77990	1	0	2710	0.0000000	0.09479	398	2927.519	13.5951280	0.251100	0	1312	13	0.9908537	0.3111	0	400	30.4878049	0.9557	1	6	1176	0.5102041	0.25900	0	81	1176.202	6.886570	0.6115	7	2928	0.2390710	0.4961	0	2.22540	0.4183	0	2.99129	0.76340	2	0.251100	0.249000	0	2.63340	0.5496	1	8.101190	0.5207	3	Yes	\$0	1	408000	\$408,000	1	Census Tract 102, Baldwin County, Alabama	23862	103200	26085	136900	27679.92	119712	-1594.92	-0.0576201	17188	0.1435779	128.38	166.27	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003010500	01003	010500	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	4230	1779	1425	498	3443	14.46413	0.3337	0	166	1625	10.215385	0.71790	0	151	1069	14.12535	0.04638	196	356	55.05618	0.73830	0	347	1425	24.35088	0.17010	707	2945	24.006791	0.7967	1	528	4001	13.19670	0.5005	0	619	14.63357	0.6436	0	790	18.67612	0.1937	0	536	3096	17.31266	0.6572	0	165	920	17.934783	0.6102	0	20	4021	0.4973887	0.32320	0	754	4230	17.825059	0.40230	0	1779	97	5.4525014	0.5525	0	8	0.4496908	0.4600	0	63	1425	4.421053	0.7762	1	90	1425	6.3157895	0.56910	0	787	4230	18.6052	0.9649	1	2.51890	0.5121	1	2.42790	0.4539	0	0.40230	0.3986	0	3.32270	0.8628	2	8.67180	0.6054	3	5877	1975	1836	820	5244	15.636918	0.3902	0	90	2583	3.484321	0.33610	0	159	1345	11.821561	0.10530	0	139	491	28.30957	0.1924	0	298	1836	16.23094	0.09053	0	570	4248	13.418079	0.6669	0	353	5247	6.727654	0.4924	0	1109	18.870172	0.6645	0	1144	19.46571	0.34110	0	717	4102.545	17.476956	0.6332	0	103	1286.1180	8.0085961	0.23410	0	0	5639	0.0000000	0.09479	868	5877.481	14.7682323	0.270900	0	1975	26	1.3164557	0.3359	0	45	2.2784810	0.6271	0	9	1836	0.4901961	0.25400	0	116	1835.798	6.318779	0.5811	633	5877	10.7708014	0.9507	1	1.97613	0.3410	0	1.96769	0.19610	0	0.270900	0.268600	0	2.74880	0.6077	1	6.963520	0.3406	1	Yes	\$0	0	0	\$0	0	Census Tract 105, Baldwin County, Alabama	21585	121100	28301	148500	25038.60	140476	3262.40	0.1302948	8024	0.0571201	191.57	213.49	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003010600	01003	010600	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3724	1440	1147	1973	3724	52.98067	0.9342	1	142	1439	9.867964	0.69680	0	235	688	34.15698	0.62950	187	459	40.74074	0.40290	0	422	1147	36.79163	0.55150	497	1876	26.492537	0.8354	1	511	3661	13.95794	0.5334	0	246	6.60580	0.1481	0	1256	33.72718	0.9305	1	496	2522	19.66693	0.7587	1	274	838	32.696897	0.8779	1	32	3479	0.9198045	0.42810	0	2606	3724	69.978518	0.81840	1	1440	21	1.4583333	0.3683	0	321	22.2916667	0.9036	1	97	1147	8.456844	0.8956	1	167	1147	14.5597210	0.82090	1	0	3724	0.0000	0.3640	0	3.55130	0.7859	2	3.14330	0.8145	3	0.81840	0.8108	1	3.35240	0.8725	3	10.86540	0.8550	9	4115	1534	1268	1676	3997	41.931449	0.8814	1	294	1809	16.252073	0.96740	1	341	814	41.891892	0.94320	1	204	454	44.93392	0.5438	0	545	1268	42.98107	0.83620	1	624	2425	25.731959	0.9002	1	994	4115	24.155529	0.9602	1	642	15.601458	0.4841	0	1126	27.36331	0.81750	1	568	2989.000	19.003011	0.7045	0	212	715.0000	29.6503497	0.85920	1	56	3825	1.4640523	0.53120	2715	4115.000	65.9781288	0.773200	1	1534	0	0.0000000	0.1079	0	529	34.4850065	0.9685	1	101	1268	7.9652997	0.87950	1	89	1268.000	7.018927	0.6184	17	4115	0.4131227	0.5707	0	4.54540	0.9754	5	3.39650	0.90810	2	0.773200	0.766700	1	3.14500	0.7858	2	11.860100	0.9520	10	Yes	\$0	1	8000000	\$8,000,000	1	Census Tract 106, Baldwin County, Alabama	17788	81600	16453	104700	20634.08	94656	-4181.08	-0.2026298	10044	0.1061105	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011000	01003	011000	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3758	2012	1576	1053	3758	28.02022	0.6597	0	66	1707	3.866432	0.16250	0	293	1297	22.59059	0.25080	83	279	29.74910	0.19030	0	376	1576	23.85787	0.15710	744	2723	27.322806	0.8465	1	996	4137	24.07542	0.8462	1	713	18.97286	0.8429	1	804	21.39436	0.3306	0	763	3295	23.15630	0.8670	1	155	1145	13.537118	0.4538	0	50	3475	1.4388489	0.51460	0	516	3758	13.730708	0.33300	0	2012	0	0.0000000	0.1224	0	606	30.1192843	0.9484	1	42	1576	2.664975	0.6476	0	96	1576	6.0913706	0.55620	0	0	3758	0.0000	0.3640	0	2.67200	0.5579	2	3.00890	0.7581	2	0.33300	0.3299	0	2.63860	0.5614	1	8.65250	0.6030	5	4921	1979	1732	1539	4908	31.356968	0.7523	1	150	2105	7.125891	0.72850	0	214	1471	14.547927	0.20260	0	59	261	22.60536	0.1167	0	273	1732	15.76212	0.07981	0	936	3332	28.091237	0.9206	1	861	4921	17.496444	0.8930	1	1039	21.113595	0.7653	1	1183	24.03983	0.64410	0	585	3738.000	15.650080	0.5371	0	81	1151.0000	7.0373588	0.19000	0	101	4546	2.2217334	0.61440	1244	4921.000	25.2794148	0.427800	0	1979	0	0.0000000	0.1079	0	527	26.6296109	0.9393	1	83	1732	4.7921478	0.77460	1	151	1732.000	8.718245	0.6904	20	4921	0.4064215	0.5688	0	3.37421	0.7528	3	2.75090	0.63780	1	0.427800	0.424200	0	3.08100	0.7597	2	9.633910	0.7366	6	Yes	\$0	0	0	\$0	0	Census Tract 110, Baldwin County, Alabama	19340	126400	23679	158700	22434.40	146624	1244.60	0.0554773	12076	0.0823603	129.69	188.85	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011406	01003	011406	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3317	6418	1307	583	3317	17.57612	0.4181	0	70	1789	3.912800	0.16690	0	221	685	32.26277	0.57540	284	622	45.65916	0.52130	0	505	1307	38.63810	0.60430	168	2255	7.450111	0.2800	0	919	3677	24.99320	0.8623	1	452	13.62677	0.5791	0	673	20.28942	0.2668	0	366	2769	13.21777	0.4276	0	96	887	10.822999	0.3359	0	180	3066	5.8708415	0.77920	1	473	3317	14.259873	0.34330	0	6418	3976	61.9507635	0.9655	1	384	5.9831723	0.7063	0	17	1307	1.300689	0.4632	0	10	1307	0.7651109	0.08684	0	0	3317	0.0000	0.3640	0	2.33160	0.4577	1	2.38860	0.4323	1	0.34330	0.3401	0	2.58584	0.5335	1	7.64934	0.4576	3	3226	7850	1797	228	3215	7.091757	0.1241	0	72	2055	3.503650	0.33910	0	302	1139	26.514486	0.69300	0	230	658	34.95441	0.3131	0	532	1797	29.60490	0.52020	0	128	2726	4.695525	0.2384	0	530	3226	16.429014	0.8749	1	790	24.488531	0.8715	1	342	10.60136	0.05624	0	280	2884.000	9.708738	0.1832	0	58	792.0000	7.3232323	0.20270	0	15	3107	0.4827808	0.34070	15	3226.000	0.4649721	0.002512	0	7850	5394	68.7133758	0.9706	1	274	3.4904459	0.6697	0	23	1797	1.2799110	0.41980	0	26	1797.000	1.446856	0.1647	0	3226	0.0000000	0.1831	0	2.09670	0.3785	1	1.65434	0.08785	1	0.002512	0.002491	0	2.40790	0.4381	1	6.161452	0.2215	3	Yes	\$0	0	0	\$0	0	Census Tract 114.06, Baldwin County, Alabama	29838	252000	32201	224200	34612.08	292320	-2411.08	-0.0696601	-68120	-0.2330323	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011407	01003	011407	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	5187	6687	2066	1404	5172	27.14617	0.6423	0	172	1935	8.888889	0.63280	0	482	1433	33.63573	0.61530	367	633	57.97788	0.79510	1	849	2066	41.09390	0.67110	278	3618	7.683803	0.2906	0	1027	4945	20.76845	0.7735	1	1398	26.95200	0.9629	1	1263	24.34933	0.5302	0	596	3792	15.71730	0.5759	0	158	1633	9.675444	0.2833	0	29	4867	0.5958496	0.35240	0	170	5187	3.277424	0.07984	0	6687	2772	41.4535666	0.9251	1	197	2.9460147	0.6326	0	90	2066	4.356244	0.7729	1	0	2066	0.0000000	0.02586	0	0	5187	0.0000	0.3640	0	3.01030	0.6516	1	2.70470	0.6077	1	0.07984	0.0791	0	2.72046	0.6014	2	8.51530	0.5852	4	5608	7576	2543	1058	5602	18.886112	0.4835	0	32	2631	1.216268	0.05882	0	581	1979	29.358262	0.77080	1	309	564	54.78723	0.7671	1	890	2543	34.99803	0.67250	0	230	4433	5.188360	0.2698	0	776	5602	13.852196	0.8156	1	1527	27.228959	0.9205	1	567	10.11056	0.05099	0	615	5035.000	12.214498	0.3295	0	16	1746.0000	0.9163803	0.01566	0	0	5573	0.0000000	0.09479	441	5608.000	7.8637660	0.140300	0	7576	3055	40.3247096	0.9148	1	72	0.9503696	0.5383	0	0	2543	0.0000000	0.09796	0	125	2543.000	4.915454	0.4934	6	5608	0.1069900	0.4054	0	2.30022	0.4418	1	1.41144	0.04295	1	0.140300	0.139100	0	2.44986	0.4589	1	6.301820	0.2416	3	Yes	\$0	0	0	\$0	0	Census Tract 114.07, Baldwin County, Alabama	22317	292600	28418	241100	25887.72	339416	2530.28	0.0977406	-98316	-0.2896622	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011502	01003	011502	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	9234	4606	3702	3160	9213	34.29936	0.7632	1	282	4002	7.046477	0.47570	0	526	2158	24.37442	0.31260	582	1544	37.69430	0.33410	0	1108	3702	29.92977	0.33740	997	6176	16.143135	0.6201	0	2074	10111	20.51231	0.7670	1	1450	15.70284	0.7043	0	2491	26.97639	0.6984	0	1542	7577	20.35106	0.7842	1	684	2718	25.165563	0.7767	1	532	8697	6.1170519	0.78590	1	3275	9234	35.466753	0.60970	0	4606	214	4.6461138	0.5268	0	828	17.9765523	0.8689	1	89	3702	2.404106	0.6192	0	293	3702	7.9146407	0.64700	0	0	9234	0.0000	0.3640	0	2.96340	0.6387	2	3.74950	0.9623	3	0.60970	0.6040	0	3.02590	0.7475	1	10.34850	0.8024	6	14165	6867	6002	2853	14165	20.141193	0.5175	0	313	7047	4.441606	0.46620	0	1181	4164	28.362152	0.74500	0	887	1838	48.25898	0.6211	0	2068	6002	34.45518	0.65900	0	1667	10750	15.506977	0.7286	0	2527	14165	17.839746	0.8980	1	3082	21.757854	0.7907	1	2506	17.69149	0.24240	0	3004	11659.000	25.765503	0.9038	1	407	3482.0000	11.6886847	0.39940	0	364	13519	2.6925068	0.65290	2755	14165.000	19.4493470	0.346300	0	6867	441	6.4220183	0.5555	0	526	7.6598223	0.7585	1	93	6002	1.5494835	0.46540	0	184	6002.000	3.065645	0.3373	0	14165	0.0000000	0.1831	0	3.26930	0.7261	1	2.98920	0.76250	2	0.346300	0.343400	0	2.29980	0.3856	1	8.904600	0.6398	4	Yes	\$0	2	8860000	\$8,860,000	1	Census Tract 115.02, Baldwin County, Alabama	20411	162700	22820	180400	23676.76	188732	-856.76	-0.0361857	-8332	-0.0441473	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380

LIHTC

svi_divisional_lihtc_df0 <- left_join(svi_divisional_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_lihtc_df1 <- left_join(svi_divisional_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_lihtc_df <- left_join(svi_divisional_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	post10_lihtc_project_cnt	post10_lihtc_project_dollars	lihtc_flag	lihtc_eligibility	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
10003002200	10003	002200	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	3765	1162	957	2101	3738	56.20653	0.9604	1	191	1458	13.1001372	0.83340	1	194	329	58.96657	0.97690	1	377	628	60.03185	0.8081	1	571	957	59.66562	0.9664	1	779	1798	43.325918	0.9854	1	493	3339	14.764900	0.46710	225	5.976096	0.11800	0	1429	37.9548473	0.989700	1	417	1970	21.167513	0.784400	1	314	714	43.97759	0.95280	1	500	3388	14.7579693	0.9426	1	3668	3765	97.423639	0.9678	1	1162	23	1.979346	0.4385	0	0.1809	134	957	14.0020899	0.9922	1	308	957	32.18391	0.9657	1	7	3765	0.185923	0.7635	1	4.21270	0.9244	4	3.787500	0.968900	4	0.9678	0.9550	1	3.3408	0.8640	3	12.308800	0.9629	12	2815	994	737	1430	2784	51.36494	0.9554	1	106	1114	9.515260	0.8545	1	143	340	42.05882	0.9621	1	155	397	39.04282	0.3733	0	298	737	40.43419	0.8083	1	606	1745	34.727794	0.9837	1	402	2815	14.280639	0.7435	503	17.868561	0.57060	846	30.053286	0.92830	1	491	1969.000	24.936516	0.88050	1	151	565.0000	26.72566	0.825600	1	560	2625	21.3333333	0.9754	1	2646	2815.000	93.99645	0.9462	1	994	11	1.10664	0.3592	0	0.18	6	737	0.8141113	0.3774	0	108	737.000	14.65400	0.8690	1	31	2815	1.1012433	0.7405	0	4.3454	0.9499	4	4.180400	0.984000	4	0.9462	0.9347	1	2.5261	0.4921	1	11.998100	0.9568	10	0	0	0	Yes	Census Tract 22, New Castle County, Delaware	14641	95400	23472	79600	16983.56	110664	6488.44	0.3820424	-31064	-0.2807056	NA	NA	New Castle County, Delaware	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428
10003014501	10003	014501	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	1955	939	777	1386	1955	70.89514	0.9895	1	48	1122	4.2780749	0.19780	0	0	60	0.00000	0.00257	0	541	717	75.45328	0.9611	1	541	777	69.62677	0.9944	1	147	618	23.786408	0.7845	1	52	2056	2.529183	0.02664	131	6.700767	0.15180	0	6	0.3069054	0.005545	0	176	2056	8.560311	0.161200	0	0	80	0.00000	0.01071	0	8	1955	0.4092072	0.2950	0	184	1955	9.411765	0.1643	0	939	370	39.403621	0.9074	1	0.1809	0	777	0.0000000	0.1488	0	146	777	18.79022	0.9044	1	0	1955	0.000000	0.3814	0	2.99284	0.6440	3	0.624255	0.003867	0	0.1643	0.1621	0	2.5229	0.4936	2	6.304295	0.2474	5	2126	1068	956	1531	2126	72.01317	0.9948	1	136	1067	12.746017	0.9370	1	17	19	89.47368	0.9990	1	638	937	68.08965	0.9311	1	655	956	68.51464	0.9967	1	36	615	5.853658	0.2880	0	175	2126	8.231421	0.4254	174	8.184384	0.10240	140	6.585136	0.03945	0	121	1986.000	6.092649	0.05007	0	37	102.0000	36.27451	0.923300	1	50	2074	2.4108004	0.6655	0	796	2126.000	37.44120	0.5142	0	1068	676	63.29588	0.9619	1	0.18	95	956	9.9372385	0.9670	1	322	956.000	33.68201	0.9751	1	0	2126	0.0000000	0.2111	0	3.6419	0.8250	3	1.780720	0.113400	1	0.5142	0.5080	0	3.2951	0.8374	3	9.231920	0.6857	7	2	0	1	Yes	Census Tract 145.01, New Castle County, Delaware	9067	325000	7380	233800	10517.72	377000	-3137.72	-0.2983270	-143200	-0.3798408	NA	NA	New Castle County, Delaware	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428
10003014502	10003	014502	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	5783	1441	1105	2275	2996	75.93458	0.9934	1	132	2389	5.5253244	0.31400	0	50	229	21.83406	0.19290	0	652	876	74.42922	0.9567	1	702	1105	63.52941	0.9830	1	33	587	5.621806	0.1831	0	180	6088	2.956636	0.03618	118	2.040463	0.02041	0	233	4.0290507	0.020260	0	151	3410	4.428153	0.029160	0	115	199	57.78894	0.98670	1	39	5711	0.6828927	0.3758	0	491	5783	8.490403	0.1472	0	1441	472	32.755031	0.8757	1	0.1809	52	1105	4.7058824	0.8627	1	242	1105	21.90045	0.9274	1	2787	5783	48.192979	0.9885	1	2.50968	0.4907	2	1.432330	0.040710	1	0.1472	0.1453	0	3.8352	0.9660	4	7.924410	0.4875	7	6752	1338	1064	2027	2989	67.81532	0.9920	1	353	2685	13.147114	0.9437	1	45	160	28.12500	0.7682	1	620	904	68.58407	0.9350	1	665	1064	62.50000	0.9903	1	52	1141	4.557406	0.2116	0	305	6727	4.533968	0.1964	201	2.976896	0.01566	103	1.525474	0.01056	0	182	2931.000	6.209485	0.05207	0	0	196.0000	0.00000	0.008258	0	33	6752	0.4887441	0.3436	0	1244	6752.000	18.42417	0.2554	0	1338	386	28.84903	0.8433	1	0.18	0	1064	0.0000000	0.1168	0	117	1064.000	10.99624	0.7959	1	3772	6752	55.8649289	0.9908	1	3.3340	0.7465	3	0.430148	0.002408	0	0.2554	0.2523	0	2.9268	0.6874	3	6.946348	0.3282	6	0	0	0	Yes	Census Tract 145.02, New Castle County, Delaware	3939	248000	3920	256700	4569.24	287680	-649.24	-0.1420893	-30980	-0.1076891	273.23	354.36	New Castle County, Delaware	Philadelphia-Camden-Vineland, PA-NJ-DE-MD CSA	CS428
11001001803	11001	001803	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	2965	1716	1585	712	2965	24.01349	0.5660	0	249	1742	14.2939150	0.86780	1	85	352	24.14773	0.27170	0	654	1233	53.04136	0.6691	0	739	1585	46.62461	0.8084	1	530	2290	23.144105	0.7689	1	621	3307	18.778349	0.64490	442	14.907251	0.64050	0	433	14.6037099	0.130700	0	397	2619	15.158457	0.509500	0	128	666	19.21922	0.63310	0	323	2875	11.2347826	0.9153	1	2807	2965	94.671164	0.9471	1	1716	1178	68.648019	0.9715	1	0.1809	92	1585	5.8044164	0.9056	1	605	1585	38.17035	0.9779	1	0	2965	0.000000	0.3814	0	3.65600	0.8220	3	2.829100	0.677100	1	0.9471	0.9346	1	3.4173	0.8900	3	10.849500	0.8633	8	4161	1765	1623	1067	4161	25.64287	0.6257	0	166	2245	7.394209	0.7467	0	60	216	27.77778	0.7579	1	682	1407	48.47193	0.5905	0	742	1623	45.71781	0.8878	1	624	2995	20.834725	0.8628	1	563	4161	13.530401	0.7154	400	9.613074	0.14970	918	22.062004	0.56160	0	717	3243.000	22.109158	0.80240	1	200	730.0000	27.39726	0.836500	1	533	3914	13.6177823	0.9433	1	3689	4161.000	88.65657	0.9109	1	1765	1317	74.61756	0.9757	1	0.18	199	1623	12.2612446	0.9830	1	690	1623.000	42.51386	0.9878	1	5	4161	0.1201634	0.4852	0	3.8384	0.8685	2	3.293500	0.887500	3	0.9109	0.8998	1	3.6117	0.9260	3	11.654500	0.9370	9	0	0	0	Yes	Census Tract 18.03, District of Columbia, District of Columbia	26314	451600	27862	572400	30524.24	523856	-2662.24	-0.0872172	48544	0.0926667	243.08	337.39	District of Columbia, District of Columbia	Washington-Baltimore-Northern Virginia, DC-MD-VA-WV CSA	CS548
11001002001	11001	002001	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	2668	1096	1004	345	2668	12.93103	0.2723	0	13	1550	0.8387097	0.02102	0	78	406	19.21182	0.12550	0	398	598	66.55518	0.8962	1	476	1004	47.41036	0.8256	1	327	2055	15.912409	0.5720	0	219	2427	9.023486	0.22340	308	11.544228	0.42830	0	457	17.1289355	0.196800	0	299	1964	15.224033	0.511800	0	221	606	36.46865	0.91070	1	127	2521	5.0376835	0.7970	1	2082	2668	78.035982	0.8691	1	1096	471	42.974453	0.9187	1	0.1809	3	1004	0.2988048	0.3128	0	342	1004	34.06374	0.9705	1	183	2668	6.859070	0.9133	1	1.91432	0.3153	1	2.844600	0.685900	2	0.8691	0.8576	1	3.2962	0.8507	3	8.924220	0.6368	7	3578	1241	1181	1230	3571	34.44413	0.7939	1	88	1925	4.571429	0.4798	0	96	340	28.23529	0.7710	1	534	841	63.49584	0.8822	1	630	1181	53.34462	0.9577	1	828	2392	34.615385	0.9832	1	137	3572	3.835386	0.1527	570	15.930687	0.47320	988	27.613192	0.86160	1	358	2588.000	13.833076	0.40220	0	188	855.0000	21.98830	0.727700	0	698	3258	21.4241866	0.9755	1	3296	3578.000	92.11850	0.9329	1	1241	838	67.52619	0.9678	1	0.18	216	1181	18.2895851	0.9962	1	490	1181.000	41.49026	0.9866	1	96	3578	2.6830632	0.8444	1	3.3673	0.7557	3	3.440200	0.924000	2	0.9329	0.9215	1	3.9750	0.9729	4	11.715400	0.9405	10	0	0	0	Yes	Census Tract 20.01, District of Columbia, District of Columbia	30587	654900	31601	840900	35480.92	759684	-3879.92	-0.1093523	81216	0.1069076	261.91	372.73	District of Columbia, District of Columbia	Washington-Baltimore-Northern Virginia, DC-MD-VA-WV CSA	CS548
11001002101	11001	002101	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	4735	2173	1797	1273	4735	26.88490	0.6333	0	380	2545	14.9312377	0.88620	1	413	910	45.38462	0.87540	1	528	887	59.52649	0.7988	1	941	1797	52.36505	0.9021	1	974	3332	29.231693	0.8912	1	1104	5530	19.963834	0.68980	511	10.791975	0.37930	0	962	20.3167899	0.326700	0	473	4252	11.124177	0.294000	0	318	997	31.89569	0.86880	1	523	4484	11.6636931	0.9194	1	4604	4735	97.233368	0.9664	1	2173	861	39.622642	0.9084	1	0.1809	60	1797	3.3388982	0.7732	1	427	1797	23.76183	0.9390	1	116	4735	2.449842	0.8331	1	4.00260	0.8910	3	2.788200	0.656100	2	0.9664	0.9536	1	3.6346	0.9380	4	11.391800	0.9101	10	5693	2360	2143	1011	5693	17.75865	0.4264	0	82	3360	2.440476	0.1930	0	390	1070	36.44860	0.9206	1	344	1073	32.05965	0.2338	0	734	2143	34.25105	0.6685	0	518	3999	12.953238	0.6323	0	320	5693	5.620938	0.2628	684	12.014755	0.25940	1440	25.294221	0.75820	1	387	4253.000	9.099459	0.15780	0	438	1260.0000	34.76190	0.914000	1	281	5250	5.3523810	0.8235	1	5097	5693.000	89.53100	0.9159	1	2360	1021	43.26271	0.9125	1	0.18	70	2143	3.2664489	0.7350	0	490	2143.000	22.86514	0.9409	1	12	5693	0.2107852	0.5502	0	2.1830	0.3950	0	2.912900	0.728400	3	0.9159	0.9047	1	3.3186	0.8452	2	9.330400	0.6994	6	2	301689	1	Yes	Census Tract 21.01, District of Columbia, District of Columbia	25269	347700	37281	535600	29312.04	403332	7968.96	0.2718664	132268	0.3279383	306.75	608.13	District of Columbia, District of Columbia	Washington-Baltimore-Northern Virginia, DC-MD-VA-WV CSA	CS548
11001002102	11001	002102	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	4331	2300	1959	844	4314	19.56421	0.4524	0	230	2296	10.0174216	0.68400	0	529	1073	49.30103	0.92220	1	427	886	48.19413	0.5547	0	956	1959	48.80041	0.8496	1	590	3048	19.356955	0.6744	0	505	4796	10.529608	0.28220	795	18.356038	0.78820	1	781	18.0327869	0.227700	0	533	3865	13.790427	0.435100	0	253	879	28.78271	0.82940	1	224	3966	5.6480081	0.8164	1	4191	4331	96.767490	0.9624	1	2300	388	16.869565	0.7553	1	0.1809	75	1959	3.8284839	0.8124	1	492	1959	25.11485	0.9448	1	0	4331	0.000000	0.3814	0	2.94260	0.6280	1	3.096800	0.806900	3	0.9624	0.9497	1	3.0748	0.7667	3	10.076600	0.7841	8	5607	2369	2132	825	5446	15.14873	0.3486	0	209	3245	6.440678	0.6748	0	278	1266	21.95893	0.5388	0	239	866	27.59815	0.1650	0	517	2132	24.24953	0.3348	0	607	4263	14.238799	0.6782	0	305	5598	5.448374	0.2520	819	14.606742	0.40290	1108	19.761013	0.41430	0	568	4496.000	12.633452	0.33400	0	131	1306.0000	10.03063	0.318400	0	396	5261	7.5270861	0.8749	1	4997	5607.000	89.12074	0.9136	1	2369	523	22.07683	0.7910	1	0.18	99	2132	4.6435272	0.8330	1	439	2132.000	20.59099	0.9275	1	25	5607	0.4458712	0.6371	0	2.2884	0.4249	0	2.344500	0.383400	1	0.9136	0.9025	1	3.3686	0.8631	3	8.915100	0.6434	5	0	0	0	Yes	Census Tract 21.02, District of Columbia, District of Columbia	30821	384700	43067	533900	35752.36	446252	7314.64	0.2045918	87648	0.1964092	258.28	488.79	District of Columbia, District of Columbia	Washington-Baltimore-Northern Virginia, DC-MD-VA-WV CSA	CS548
11001002400	11001	002400	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	3771	1371	1326	735	3771	19.49085	0.4500	0	351	2184	16.0714286	0.91120	1	211	911	23.16136	0.23540	0	212	415	51.08434	0.6239	0	423	1326	31.90045	0.3851	0	547	2913	18.777892	0.6600	0	442	3652	12.102957	0.34850	644	17.077698	0.74380	0	597	15.8313445	0.157300	0	412	2993	13.765453	0.433500	0	131	750	17.46667	0.57510	0	92	3658	2.5150355	0.6532	0	3316	3771	87.934235	0.9116	1	1371	232	16.921955	0.7557	1	0.1809	40	1326	3.0165913	0.7456	0	458	1326	34.53997	0.9721	1	0	3771	0.000000	0.3814	0	2.75480	0.5675	1	2.562900	0.528200	0	0.9116	0.8996	1	3.0357	0.7509	2	9.265000	0.6847	4	4059	1435	1356	549	4015	13.67372	0.3051	0	105	2778	3.779698	0.3758	0	159	876	18.15068	0.3556	0	152	481	31.60083	0.2269	0	311	1357	22.91820	0.2843	0	775	3420	22.660819	0.8954	1	337	4003	8.418686	0.4368	486	11.973392	0.25750	448	11.037201	0.08946	0	357	3565.455	10.012749	0.19900	0	101	685.6722	14.73007	0.508900	0	660	3925	16.8152866	0.9608	1	2687	4059.093	66.19705	0.7815	1	1435	376	26.20209	0.8256	1	0.18	104	1356	7.6696165	0.9358	1	328	1356.438	24.18097	0.9469	1	72	4059	1.7738359	0.7960	1	2.2974	0.4283	1	2.015660	0.204600	1	0.7815	0.7720	1	3.6843	0.9401	4	8.778860	0.6239	7	0	0	0	Yes	Census Tract 24, District of Columbia, District of Columbia	31431	410000	46985	623600	36459.96	475600	10525.04	0.2886739	148000	0.3111859	320.66	611.78	District of Columbia, District of Columbia	Washington-Baltimore-Northern Virginia, DC-MD-VA-WV CSA	CS548
11001002701	11001	002701	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	5414	2927	2716	974	5224	18.64472	0.4281	0	151	4012	3.7637089	0.15460	0	221	638	34.63950	0.64400	0	973	2078	46.82387	0.5234	0	1194	2716	43.96171	0.7465	0	579	3999	14.478620	0.5249	0	546	4681	11.664174	0.33000	378	6.981899	0.16380	0	586	10.8237902	0.070390	0	102	4153	2.456056	0.007624	0	229	827	27.69045	0.81270	1	198	5197	3.8098903	0.7406	0	2851	5414	52.659771	0.7261	0	2927	2081	71.096686	0.9751	1	0.1809	167	2716	6.1487482	0.9168	1	1275	2716	46.94404	0.9890	1	190	5414	3.509420	0.8632	1	2.18410	0.3915	0	1.795114	0.123700	1	0.7261	0.7165	0	3.9250	0.9729	4	8.630314	0.5962	5	6651	2920	2747	733	6508	11.26306	0.2337	0	101	4736	2.132601	0.1533	0	122	779	15.66110	0.2359	0	900	1968	45.73171	0.5282	0	1022	2747	37.20422	0.7403	0	612	4924	12.428920	0.6126	0	369	6508	5.669945	0.2662	385	5.788603	0.04676	1117	16.794467	0.25460	0	389	5391.000	7.215730	0.08129	0	234	895.0000	26.14525	0.815400	1	109	6138	1.7758227	0.5949	0	3988	6651.000	59.96091	0.7366	0	2920	1982	67.87671	0.9685	1	0.18	247	2747	8.9916272	0.9563	1	1315	2747.000	47.87040	0.9925	1	143	6651	2.1500526	0.8197	1	2.0061	0.3411	0	1.792950	0.117000	1	0.7366	0.7276	0	3.9170	0.9684	4	8.452650	0.5694	5	0	0	0	Yes	Census Tract 27.01, District of Columbia, District of Columbia	33454	563600	52608	713500	38806.64	653776	13801.36	0.3556443	59724	0.0913524	NA	NA	District of Columbia, District of Columbia	Washington-Baltimore-Northern Virginia, DC-MD-VA-WV CSA	CS548
11001002900	11001	002900	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	3885	1767	1384	1193	3885	30.70785	0.7107	0	281	2376	11.8265993	0.78030	1	260	578	44.98270	0.86970	1	323	806	40.07444	0.3674	0	583	1384	42.12428	0.7027	0	838	2803	29.896539	0.9003	1	424	4641	9.135962	0.22670	213	5.482626	0.09811	0	744	19.1505792	0.270000	0	325	3732	8.708467	0.168500	0	193	619	31.17932	0.86080	1	229	3681	6.2211356	0.8321	1	3004	3885	77.323037	0.8656	1	1767	586	33.163554	0.8781	1	0.1809	162	1384	11.7052023	0.9841	1	517	1384	37.35549	0.9768	1	0	3885	0.000000	0.3814	0	3.32070	0.7423	2	2.229510	0.333000	2	0.8656	0.8542	1	3.4013	0.8852	3	9.817110	0.7534	8	4450	1697	1486	529	4416	11.97917	0.2571	0	57	3378	1.687389	0.1054	0	152	877	17.33181	0.3135	0	188	609	30.87028	0.2143	0	340	1486	22.88022	0.2828	0	405	3531	11.469839	0.5689	0	263	4441	5.922090	0.2820	245	5.505618	0.04174	716	16.089888	0.22520	0	271	3734.000	7.257633	0.08277	0	177	747.0000	23.69478	0.765800	1	329	4197	7.8389326	0.8803	1	2470	4450.000	55.50562	0.6970	0	1697	509	29.99411	0.8494	1	0.18	61	1486	4.1049798	0.7992	1	473	1486.000	31.83042	0.9709	1	16	4450	0.3595506	0.6142	0	1.4962	0.1947	0	1.995810	0.196000	2	0.6970	0.6885	0	3.4137	0.8771	3	7.602710	0.4367	5	0	0	0	Yes	Census Tract 29, District of Columbia, District of Columbia	29779	506200	57209	678700	34543.64	587192	22665.36	0.6561370	91508	0.1558400	372.28	614.06	District of Columbia, District of Columbia	Washington-Baltimore-Northern Virginia, DC-MD-VA-WV CSA	CS548

svi_national_lihtc_df0 <- left_join(svi_national_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_lihtc_df1 <- left_join(svi_national_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_lihtc_df <- left_join(svi_national_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	lihtc_eligibility	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
01005950700	01005	950700	AL	Alabama	Barbour County	3	South Region	6	East South Central Division	1753	687	563	615	1628	37.77641	0.8088	1	17	667	2.548726	0.06941	0	41	376	10.90426	0.01945	0	62	187	33.15508	0.24640	0	103	563	18.29485	0.04875	0	264	1208	21.85430	0.7570	1	201	1527	13.163065	0.4991	0	368	20.992584	0.89510	1	462	26.354820	0.66130	211	1085	19.44700	0.7505	1	107	399	26.81704	0.8048	1	0	1628	0.0000000	0.09298	861	1753	49.11580	0.7101	0	687	17	2.4745269	0.4324	0	38	5.5312955	0.6970	0	3	563	0.5328597	0.3037	0	19	563	3.374778	0.3529	0	233	1753	13.29150	0.9517	1	2.18306	0.4137	2	3.20468	0.8377	3	0.7101	0.7035	0	2.7377	0.6100	1	8.83554	0.6264	6	1527	691	595	565	1365	41.39194	0.8765	1	37	572	6.468532	0.6776	0	70	376	18.617021	0.38590	0	92	219	42.009132	0.47360	0	162	595	27.22689	0.44540	0	280	1114	25.13465	0.8942	1	105	1378	7.619739	0.5505	0	383	25.081860	0.88450	1	337	22.069417	0.51380	237	1041.0000	22.76657	0.8360	1	144	413.0000	34.86683	0.9114	1	11	1466	0.7503411	0.40700	711	1527.0000	46.56189	0.6441	0	691	13	1.8813314	0.3740	0	37	5.3545586	0.7152	0	0	595	0.0000000	0.09796	115	595.0000	19.327731	0.8859	1	149	1527	9.7576948	0.9470	1	3.44420	0.7707	2	3.55270	0.9403	3	0.6441	0.6387	0	3.02006	0.7337	2	10.66106	0.8537	7	Yes	Census Tract 9507, Barbour County, Alabama	15257	133700	17244	137500	17698.12	155092	-454.12	-0.0256592	-17592	-0.1134294	131.05	135.61	Barbour County, Alabama	Eufaula, AL-GA MicroSA	C2164
01011952100	01011	952100	AL	Alabama	Bullock County	3	South Region	6	East South Central Division	1652	796	554	564	1652	34.14044	0.7613	1	46	816	5.637255	0.33630	0	96	458	20.96070	0.19930	0	62	96	64.58333	0.89170	1	158	554	28.51986	0.29220	0	271	1076	25.18587	0.8163	1	155	1663	9.320505	0.3183	0	199	12.046005	0.47180	0	420	25.423729	0.60240	327	1279	25.56685	0.9151	1	137	375	36.53333	0.9108	1	0	1590	0.0000000	0.09298	1428	1652	86.44068	0.8939	1	796	0	0.0000000	0.1224	0	384	48.2412060	0.9897	1	19	554	3.4296029	0.7145	0	45	554	8.122744	0.6556	0	0	1652	0.00000	0.3640	0	2.52440	0.5138	2	2.99308	0.7515	2	0.8939	0.8856	1	2.8462	0.6637	1	9.25758	0.6790	6	1382	748	549	742	1382	53.69030	0.9560	1	40	511	7.827789	0.7730	1	110	402	27.363184	0.71780	0	45	147	30.612245	0.23070	0	155	549	28.23315	0.47730	0	181	905	20.00000	0.8253	1	232	1382	16.787265	0.8813	1	164	11.866860	0.27170	0	250	18.089725	0.26290	258	1132.0000	22.79152	0.8368	1	99	279.0000	35.48387	0.9162	1	33	1275	2.5882353	0.64520	1347	1382.0000	97.46744	0.9681	1	748	0	0.0000000	0.1079	0	375	50.1336898	0.9922	1	0	549	0.0000000	0.09796	37	549.0000	6.739526	0.6039	0	0	1382	0.0000000	0.1831	0	3.91290	0.8785	4	2.93280	0.7342	2	0.9681	0.9599	1	1.98506	0.2471	1	9.79886	0.7570	8	Yes	Census Tract 9521, Bullock County, Alabama	19754	58200	18598	66900	22914.64	67512	-4316.64	-0.1883791	-612	-0.0090651	NA	NA	NA	NA	NA
01015000300	01015	000300	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3074	1635	1330	1904	3067	62.08021	0.9710	1	293	1362	21.512482	0.96630	1	180	513	35.08772	0.65450	0	383	817	46.87882	0.55040	0	563	1330	42.33083	0.70280	0	720	2127	33.85049	0.9148	1	628	2835	22.151675	0.8076	1	380	12.361744	0.49340	0	713	23.194535	0.45030	647	2111	30.64898	0.9708	1	298	773	38.55110	0.9247	1	0	2878	0.0000000	0.09298	2623	3074	85.32856	0.8883	1	1635	148	9.0519878	0.6465	0	6	0.3669725	0.4502	0	68	1330	5.1127820	0.8082	1	303	1330	22.781955	0.9029	1	0	3074	0.00000	0.3640	0	4.36250	0.9430	4	2.93218	0.7233	2	0.8883	0.8800	1	3.1718	0.8070	2	11.35478	0.9009	9	2390	1702	1282	1287	2390	53.84937	0.9566	1	102	1066	9.568480	0.8541	1	158	609	25.944171	0.67520	0	286	673	42.496285	0.48560	0	444	1282	34.63339	0.66340	0	467	1685	27.71513	0.9180	1	369	2379	15.510719	0.8562	1	342	14.309623	0.40850	0	548	22.928870	0.57100	647	1831.0000	35.33588	0.9862	1	202	576.0000	35.06944	0.9130	1	16	2134	0.7497657	0.40690	1896	2390.0000	79.33054	0.8451	1	1702	96	5.6404230	0.5329	0	0	0.0000000	0.2186	0	0	1282	0.0000000	0.09796	186	1282.0000	14.508580	0.8308	1	43	2390	1.7991632	0.7727	1	4.24830	0.9395	4	3.28560	0.8773	2	0.8451	0.8379	1	2.45296	0.4602	2	10.83196	0.8718	9	Yes	Census Tract 3, Calhoun County, Alabama	12211	41700	18299	51300	14164.76	48372	4134.24	0.2918680	2928	0.0605309	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015000500	01015	000500	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	1731	1175	743	1042	1619	64.36072	0.9767	1	124	472	26.271186	0.98460	1	136	461	29.50108	0.48970	0	163	282	57.80142	0.79190	1	299	743	40.24226	0.64910	0	340	1270	26.77165	0.8389	1	460	1794	25.641026	0.8722	1	271	15.655690	0.70190	0	368	21.259388	0.32190	507	1449	34.98965	0.9885	1	150	386	38.86010	0.9269	1	0	1677	0.0000000	0.09298	1559	1731	90.06355	0.9123	1	1175	50	4.2553191	0.5128	0	4	0.3404255	0.4480	0	0	743	0.0000000	0.1238	0	122	743	16.419919	0.8473	1	0	1731	0.00000	0.3640	0	4.32150	0.9362	4	3.03218	0.7679	2	0.9123	0.9038	1	2.2959	0.3818	1	10.56188	0.8244	8	940	907	488	586	940	62.34043	0.9815	1	59	297	19.865320	0.9833	1	100	330	30.303030	0.79220	1	58	158	36.708861	0.34970	0	158	488	32.37705	0.60200	0	199	795	25.03145	0.8930	1	118	940	12.553192	0.7770	1	246	26.170213	0.90530	1	118	12.553192	0.08233	383	822.5089	46.56484	0.9984	1	30	197.8892	15.16000	0.5363	0	0	889	0.0000000	0.09479	898	940.3866	95.49264	0.9489	1	907	0	0.0000000	0.1079	0	2	0.2205072	0.4456	0	2	488	0.4098361	0.23670	146	487.6463	29.939736	0.9404	1	0	940	0.0000000	0.1831	0	4.23680	0.9379	4	2.61712	0.5593	2	0.9489	0.9409	1	1.91370	0.2196	1	9.71652	0.7468	8	Yes	Census Tract 5, Calhoun County, Alabama	11742	38800	13571	38800	13620.72	45008	-49.72	-0.0036503	-6208	-0.1379310	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015000600	01015	000600	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	2571	992	796	1394	2133	65.35396	0.9789	1	263	905	29.060773	0.98990	1	121	306	39.54248	0.75940	1	209	490	42.65306	0.44810	0	330	796	41.45729	0.68030	0	641	1556	41.19537	0.9554	1	416	1760	23.636364	0.8383	1	220	8.556982	0.24910	0	584	22.714897	0.41610	539	1353	39.83740	0.9955	1	243	466	52.14592	0.9783	1	30	2366	1.2679628	0.48990	1944	2571	75.61260	0.8440	1	992	164	16.5322581	0.7673	1	8	0.8064516	0.5110	0	46	796	5.7788945	0.8329	1	184	796	23.115578	0.9049	1	614	2571	23.88176	0.9734	1	4.44280	0.9548	4	3.12890	0.8088	2	0.8440	0.8362	1	3.9895	0.9792	4	12.40520	0.9696	11	1950	964	719	837	1621	51.63479	0.9467	1	157	652	24.079755	0.9922	1	22	364	6.043956	0.01547	0	129	355	36.338028	0.34200	0	151	719	21.00139	0.23030	0	363	1387	26.17159	0.9048	1	351	1613	21.760694	0.9435	1	249	12.769231	0.32090	0	356	18.256410	0.27140	332	1259.7041	26.35540	0.9135	1	136	435.6156	31.22018	0.8775	1	0	1891	0.0000000	0.09479	1463	1949.9821	75.02633	0.8219	1	964	14	1.4522822	0.3459	0	8	0.8298755	0.5269	0	19	719	2.6425591	0.61120	197	719.0542	27.397100	0.9316	1	329	1950	16.8717949	0.9655	1	4.01750	0.9001	4	2.47809	0.4764	2	0.8219	0.8149	1	3.38110	0.8712	2	10.69859	0.8583	9	Yes	Census Tract 6, Calhoun County, Alabama	10958	48000	14036	43300	12711.28	55680	1324.72	0.1042161	-12380	-0.2223420	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015002101	01015	002101	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3872	1454	1207	1729	2356	73.38710	0.9916	1	489	2020	24.207921	0.97860	1	20	168	11.90476	0.02541	0	718	1039	69.10491	0.93320	1	738	1207	61.14333	0.96900	1	113	725	15.58621	0.6035	0	664	3943	16.839970	0.6495	0	167	4.313016	0.05978	0	238	6.146694	0.02255	264	2359	11.19118	0.3027	0	94	263	35.74144	0.9050	1	46	3769	1.2204829	0.48250	1601	3872	41.34814	0.6572	0	1454	761	52.3383769	0.9504	1	65	4.4704264	0.6738	0	5	1207	0.4142502	0.2791	0	113	1207	9.362055	0.7004	0	1516	3872	39.15289	0.9860	1	4.19220	0.9133	3	1.77253	0.1304	1	0.6572	0.6511	0	3.5897	0.9337	2	10.21163	0.7885	6	3238	1459	1014	1082	1836	58.93246	0.9735	1	251	1403	17.890235	0.9767	1	31	155	20.000000	0.44920	0	515	859	59.953434	0.85540	1	546	1014	53.84615	0.95350	1	134	916	14.62882	0.7033	0	251	3238	7.751699	0.5588	0	167	5.157505	0.03597	0	169	5.219271	0.02111	323	1667.0000	19.37612	0.7205	0	94	277.0000	33.93502	0.9040	1	0	3164	0.0000000	0.09479	1045	3238.0000	32.27301	0.5125	0	1459	607	41.6038382	0.9185	1	65	4.4551062	0.6949	0	24	1014	2.3668639	0.57900	85	1014.0000	8.382643	0.6775	0	1402	3238	43.2983323	0.9876	1	4.16580	0.9263	3	1.77637	0.1225	1	0.5125	0.5082	0	3.85750	0.9661	2	10.31217	0.8160	6	Yes	Census Tract 21.01, Calhoun County, Alabama	4968	92000	9312	153500	5762.88	106720	3549.12	0.6158587	46780	0.4383433	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015002300	01015	002300	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3882	1861	1608	1366	3882	35.18805	0.7753	1	186	1539	12.085770	0.80740	1	284	1109	25.60866	0.35530	0	202	499	40.48096	0.39670	0	486	1608	30.22388	0.34700	0	727	2610	27.85441	0.8534	1	547	3706	14.759849	0.5669	0	716	18.444101	0.82530	1	904	23.286966	0.45720	719	2919	24.63172	0.8986	1	207	1191	17.38035	0.5923	0	0	3720	0.0000000	0.09298	490	3882	12.62236	0.3118	0	1861	38	2.0419130	0.4070	0	199	10.6931757	0.7836	1	52	1608	3.2338308	0.6986	0	166	1608	10.323383	0.7304	0	0	3882	0.00000	0.3640	0	3.35000	0.7384	3	2.86638	0.6919	2	0.3118	0.3089	0	2.9836	0.7289	1	9.51178	0.7100	6	3265	1774	1329	1103	3265	33.78254	0.7880	1	122	1422	8.579465	0.8131	1	101	844	11.966825	0.10960	0	126	485	25.979381	0.15930	0	227	1329	17.08051	0.11070	0	267	2122	12.58247	0.6388	0	328	3265	10.045942	0.6808	0	440	13.476263	0.36070	0	843	25.819296	0.74470	530	2422.0000	21.88274	0.8097	1	254	861.0000	29.50058	0.8574	1	0	3026	0.0000000	0.09479	811	3265.0000	24.83920	0.4221	0	1774	7	0.3945885	0.2444	0	338	19.0529876	0.8924	1	19	1329	1.4296464	0.44520	120	1329.0000	9.029345	0.7016	0	0	3265	0.0000000	0.1831	0	3.03140	0.6608	2	2.86729	0.7016	2	0.4221	0.4185	0	2.46670	0.4669	1	8.78749	0.6230	5	Yes	Census Tract 23, Calhoun County, Alabama	15086	77500	21540	78500	17499.76	89900	4040.24	0.2308740	-11400	-0.1268076	120.54	131.82	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01023956700	01023	956700	AL	Alabama	Choctaw County	3	South Region	6	East South Central Division	3011	1772	1179	1715	3011	56.95782	0.9531	1	266	890	29.887640	0.99100	1	267	1035	25.79710	0.36240	0	79	144	54.86111	0.73440	0	346	1179	29.34690	0.31850	0	738	2053	35.94739	0.9287	1	543	2904	18.698347	0.7133	0	569	18.897376	0.84040	1	648	21.521089	0.33840	813	2273	35.76771	0.9901	1	252	771	32.68482	0.8778	1	0	2880	0.0000000	0.09298	2455	3011	81.53437	0.8712	1	1772	38	2.1444695	0.4136	0	485	27.3702032	0.9349	1	72	1179	6.1068702	0.8435	1	109	1179	9.245123	0.6964	0	0	3011	0.00000	0.3640	0	3.90460	0.8597	3	3.13968	0.8131	3	0.8712	0.8631	1	3.2524	0.8387	2	11.16788	0.8840	9	3335	1912	1362	1135	3313	34.25898	0.7948	1	188	1147	16.390584	0.9686	1	212	1058	20.037807	0.45090	0	27	304	8.881579	0.02679	0	239	1362	17.54772	0.12350	0	466	2537	18.36815	0.7948	1	495	3335	14.842579	0.8413	1	791	23.718141	0.85250	1	613	18.380810	0.27840	884	2714.0000	32.57185	0.9752	1	230	918.0000	25.05447	0.7925	1	25	3103	0.8056719	0.41920	2637	3335.0000	79.07046	0.8436	1	1912	0	0.0000000	0.1079	0	758	39.6443515	0.9799	1	16	1362	1.1747430	0.40060	75	1362.0000	5.506608	0.5316	0	8	3335	0.2398801	0.4965	0	3.52300	0.7901	4	3.31780	0.8870	3	0.8436	0.8365	1	2.51650	0.4924	1	10.20090	0.8033	9	Yes	Census Tract 9567, Choctaw County, Alabama	12737	60900	16852	63400	14774.92	70644	2077.08	0.1405815	-7244	-0.1025423	NA	NA	NA	NA	NA
01023957000	01023	957000	AL	Alabama	Choctaw County	3	South Region	6	East South Central Division	2567	1187	916	767	2567	29.87924	0.6933	0	145	1060	13.679245	0.86050	1	101	719	14.04729	0.04540	0	43	197	21.82741	0.09791	0	144	916	15.72052	0.02333	0	355	1704	20.83333	0.7366	0	289	2296	12.587108	0.4736	0	324	12.621737	0.51120	0	688	26.801714	0.68810	572	1746	32.76060	0.9809	1	121	636	19.02516	0.6414	0	5	2283	0.2190101	0.22520	1314	2567	51.18816	0.7225	0	1187	0	0.0000000	0.1224	0	335	28.2224094	0.9394	1	13	916	1.4192140	0.4834	0	70	916	7.641921	0.6353	0	0	2567	0.00000	0.3640	0	2.78733	0.5903	1	3.04680	0.7745	1	0.7225	0.7158	0	2.5445	0.5114	1	9.10113	0.6601	3	2077	1158	866	759	2072	36.63127	0.8256	1	61	780	7.820513	0.7726	1	106	735	14.421769	0.19760	0	11	131	8.396947	0.02525	0	117	866	13.51039	0.04053	0	351	1464	23.97541	0.8815	1	205	2077	9.870005	0.6729	0	402	19.354839	0.68820	0	496	23.880597	0.63430	466	1576.0000	29.56853	0.9544	1	154	612.0000	25.16340	0.7942	1	0	2002	0.0000000	0.09479	1018	2077.0000	49.01300	0.6638	0	1158	0	0.0000000	0.1079	0	439	37.9101900	0.9766	1	0	866	0.0000000	0.09796	42	866.0000	4.849884	0.4884	0	5	2077	0.2407318	0.4971	0	3.19313	0.7061	3	3.16589	0.8369	2	0.6638	0.6582	0	2.16796	0.3247	1	9.19078	0.6792	6	Yes	Census Tract 9570, Choctaw County, Alabama	16224	51600	21740	74000	18819.84	59856	2920.16	0.1551639	14144	0.2363005	NA	NA	NA	NA	NA
01031010500	01031	010500	AL	Alabama	Coffee County	3	South Region	6	East South Central Division	4529	1950	1664	1649	4022	40.99950	0.8432	1	114	1424	8.005618	0.56260	0	309	1057	29.23368	0.48130	0	251	607	41.35091	0.41690	0	560	1664	33.65385	0.45740	0	1269	3370	37.65579	0.9387	1	516	4279	12.058892	0.4492	0	832	18.370501	0.82310	1	894	19.739457	0.23950	1023	3404	30.05288	0.9666	1	303	1112	27.24820	0.8108	1	43	4270	1.0070258	0.44510	1761	4529	38.88276	0.6383	0	1950	6	0.3076923	0.2576	0	276	14.1538462	0.8279	1	8	1664	0.4807692	0.2925	0	125	1664	7.512019	0.6289	0	507	4529	11.19452	0.9441	1	3.25110	0.7138	2	3.28510	0.8639	3	0.6383	0.6324	0	2.9510	0.7136	2	10.12550	0.7794	7	4815	2118	1731	1329	4470	29.73154	0.7256	0	147	1903	7.724645	0.7670	1	209	1256	16.640127	0.29310	0	208	475	43.789474	0.51620	0	417	1731	24.09012	0.33700	0	953	3728	25.56330	0.8985	1	668	4485	14.894091	0.8425	1	1053	21.869159	0.79500	1	766	15.908619	0.16760	1010	3719.0000	27.15784	0.9262	1	243	1133.0000	21.44748	0.7184	0	1	4577	0.0218484	0.19150	1643	4815.0000	34.12253	0.5321	0	2118	0	0.0000000	0.1079	0	475	22.4268178	0.9157	1	37	1731	2.1374928	0.55080	144	1731.0000	8.318891	0.6750	0	330	4815	6.8535826	0.9282	1	3.57060	0.8018	3	2.79870	0.6649	2	0.5321	0.5276	0	3.17760	0.7990	2	10.07900	0.7892	7	Yes	Census Tract 105, Coffee County, Alabama	14641	88000	21367	78100	16983.56	102080	4383.44	0.2580990	-23980	-0.2349138	128.88	137.26	Coffee County, Alabama	Dothan-Enterprise-Ozark, AL CSA	CS222

Log NMTC and LIHTC Variables

svi_national_nmtc_df$Median_Income_10adj_log <- log(svi_national_nmtc_df$Median_Income_10adj)
svi_national_nmtc_df$Median_Income_19_log <- log(svi_national_nmtc_df$Median_Income_19)

svi_national_nmtc_df$Median_Home_Value_10adj_log = log(svi_national_nmtc_df$Median_Home_Value_10adj)
svi_national_nmtc_df$Median_Home_Value_19_log = log(svi_national_nmtc_df$Median_Home_Value_19)

svi_national_nmtc_df$housing_price_index10_log = log(svi_national_nmtc_df$housing_price_index10)
svi_national_nmtc_df$housing_price_index20_log = log(svi_national_nmtc_df$housing_price_index20)

svi_divisional_nmtc_df$Median_Income_10adj_log <- log(svi_divisional_nmtc_df$Median_Income_10adj)
svi_divisional_nmtc_df$Median_Income_19_log <- log(svi_divisional_nmtc_df$Median_Income_19)

svi_divisional_nmtc_df$Median_Home_Value_10adj_log = log(svi_divisional_nmtc_df$Median_Home_Value_10adj)
svi_divisional_nmtc_df$Median_Home_Value_19_log = log(svi_divisional_nmtc_df$Median_Home_Value_19)

svi_divisional_nmtc_df$housing_price_index10_log = log(svi_divisional_nmtc_df$housing_price_index10)
svi_divisional_nmtc_df$housing_price_index20_log = log(svi_divisional_nmtc_df$housing_price_index20)

svi_national_lihtc_df$Median_Income_10adj_log <- log(svi_national_lihtc_df$Median_Income_10adj)
svi_national_lihtc_df$Median_Income_19_log <- log(svi_national_lihtc_df$Median_Income_19)

svi_national_lihtc_df$Median_Home_Value_10adj_log = log(svi_national_lihtc_df$Median_Home_Value_10adj)
svi_national_lihtc_df$Median_Home_Value_19_log = log(svi_national_lihtc_df$Median_Home_Value_19)

svi_national_lihtc_df$housing_price_index10_log = log(svi_national_lihtc_df$housing_price_index10)
svi_national_lihtc_df$housing_price_index20_log = log(svi_national_lihtc_df$housing_price_index20)

svi_divisional_lihtc_df$Median_Income_10adj_log <- log(svi_divisional_lihtc_df$Median_Income_10adj)
svi_divisional_lihtc_df$Median_Income_19_log <- log(svi_divisional_lihtc_df$Median_Income_19)

svi_divisional_lihtc_df$Median_Home_Value_10adj_log = log(svi_divisional_lihtc_df$Median_Home_Value_10adj)
svi_divisional_lihtc_df$Median_Home_Value_19_log = log(svi_divisional_lihtc_df$Median_Home_Value_19)

svi_divisional_lihtc_df$housing_price_index10_log = log(svi_divisional_lihtc_df$housing_price_index10)
svi_divisional_lihtc_df$housing_price_index20_log = log(svi_divisional_lihtc_df$housing_price_index20)

Diff-in-Diff Models

NMTC Evaluation

National SVI

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
nmtc_did10_usa_svi <- svi_national_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(nmtc_did10_usa_svi)

## [1] 29068

nmtc_did10_usa_svi %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	cbsa	SVI_FLAG_COUNT_SES	SVI_FLAG_COUNT_HHCHAR	SVI_FLAG_COUNT_REM	SVI_FLAG_COUNT_HOUSETRANSPT	SVI_FLAG_COUNT_OVERALL	treat	year
01001020200	Montgomery-Alexander City, AL CSA	1	3	1	1	6	0	2010
01001020700	Montgomery-Alexander City, AL CSA	0	2	0	1	3	0	2010
01001021100	Montgomery-Alexander City, AL CSA	2	1	1	1	5	0	2010
01003010200	Mobile-Daphne-Fairhope, AL CSA	1	1	0	1	3	1	2010
01003010500	Mobile-Daphne-Fairhope, AL CSA	1	0	0	2	3	0	2010
01003010600	Mobile-Daphne-Fairhope, AL CSA	2	3	1	3	9	1	2010

# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
nmtc_did20_usa_svi <- svi_national_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
         )


nrow(nmtc_did20_usa_svi)

## [1] 29068

nmtc_did20_usa_svi %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	cbsa	SVI_FLAG_COUNT_SES	SVI_FLAG_COUNT_HHCHAR	SVI_FLAG_COUNT_REM	SVI_FLAG_COUNT_HOUSETRANSPT	SVI_FLAG_COUNT_OVERALL	treat	post	year
01001020200	Montgomery-Alexander City, AL CSA	0	0	1	1	2	0	1	2020
01001020700	Montgomery-Alexander City, AL CSA	4	2	0	3	9	0	1	2020
01001021100	Montgomery-Alexander City, AL CSA	3	2	0	2	7	0	1	2020
01003010200	Mobile-Daphne-Fairhope, AL CSA	0	2	0	1	3	1	1	2020
01003010500	Mobile-Daphne-Fairhope, AL CSA	0	0	0	1	1	0	1	2020
01003010600	Mobile-Daphne-Fairhope, AL CSA	5	2	1	2	10	1	1	2020

nmtc_diff_in_diff_usa_svi <- bind_rows(nmtc_did10_usa_svi, nmtc_did20_usa_svi)

nmtc_diff_in_diff_usa_svi <- nmtc_diff_in_diff_usa_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_usa_svi)

## [1] 58136

National Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
nmtc_did10_usa_inc <- svi_national_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(nmtc_did10_usa_inc)

## [1] 29055

# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
nmtc_did19_usa_inc <- svi_national_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(nmtc_did19_usa_inc)

## [1] 29055

nmtc_diff_in_diff_usa_inc <- bind_rows(nmtc_did10_usa_inc, nmtc_did19_usa_inc)

nmtc_diff_in_diff_usa_inc <- nmtc_diff_in_diff_usa_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_usa_inc)

## [1] 58110

National Median Home Value

nmtc_did10_usa_mhv <- svi_national_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(nmtc_did10_usa_mhv)

## [1] 28199

nmtc_did19_usa_mhv <- svi_national_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(nmtc_did19_usa_mhv)

## [1] 28199

nmtc_diff_in_diff_usa_mhv <- bind_rows(nmtc_did10_usa_mhv, nmtc_did19_usa_mhv)

nmtc_diff_in_diff_usa_mhv <- nmtc_diff_in_diff_usa_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_usa_mhv)

## [1] 56398

National House Price Index

nmtc_did10_usa_hpi <- svi_national_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(nmtc_did10_usa_hpi)

## [1] 13611

nmtc_did20_usa_hpi <- svi_national_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(nmtc_did20_usa_hpi)

## [1] 13611

nmtc_diff_in_diff_usa_hpi <- bind_rows(nmtc_did10_usa_hpi, nmtc_did20_usa_hpi)

nmtc_diff_in_diff_usa_hpi <- nmtc_diff_in_diff_usa_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_usa_hpi)

## [1] 27222

NMTC Divisional

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
nmtc_did10_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(nmtc_did10_div_svi)

## [1] 5889

# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
nmtc_did20_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(nmtc_did20_div_svi)

## [1] 5889

nmtc_diff_in_diff_div_svi <- bind_rows(nmtc_did10_div_svi, nmtc_did20_div_svi)

nmtc_diff_in_diff_div_svi <- nmtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_svi)

## [1] 11778

Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
nmtc_did10_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(nmtc_did10_div_inc)

## [1] 5885

# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
nmtc_did19_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(nmtc_did19_div_inc)

## [1] 5885

nmtc_diff_in_diff_div_inc <- bind_rows(nmtc_did10_div_inc, nmtc_did19_div_inc)

nmtc_diff_in_diff_div_inc <- nmtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_inc)

## [1] 11770

Home Value

nmtc_did10_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(nmtc_did10_div_mhv)

## [1] 5768

nmtc_did19_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(nmtc_did19_div_mhv)

## [1] 5768

nmtc_diff_in_diff_div_mhv <- bind_rows(nmtc_did10_div_mhv, nmtc_did19_div_mhv)

nmtc_diff_in_diff_div_mhv <- nmtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_mhv)

## [1] 11536

House Price Index

nmtc_did10_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(nmtc_did10_div_hpi)

## [1] 2761

nmtc_did20_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(nmtc_did20_div_hpi)

## [1] 2761

nmtc_diff_in_diff_div_hpi <- bind_rows(nmtc_did10_div_hpi, nmtc_did20_div_hpi)

nmtc_diff_in_diff_div_hpi <- nmtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_hpi)

## [1] 5522

Analysis

For this evaluation, we employ a difference-in-differences (diff-in-diff) model to estimate the impact of federal tax credit programs on socioeconomic outcomes. The diff-in-diff approach allows us to compare changes over time between areas that received interventions and those that did not. We will be comparing groups from both Low-Income Housing Tax Credits (LIHTC) and New Markets Tax Credits (NMTC).

Our dependent variables capture key measures of tract well-being:

Social Vulnerability Index
Median Household Income
Median Home Value
House Price Index

These variables provide a comprehensive view of both economic and social changes in the communities studied. The independent variables focus on the presence and intensity of LIHTC and NMTC allocations, representing targeted federal investments intended to stimulate housing affordability and economic development in underserved areas.

NMTC Divisional Models

# SVI & Economic Models

m1_nmtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m2_nmtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m3_nmtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m4_nmtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m5_nmtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi)

m6_nmtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_inc )

m7_nmtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_mhv )

m8_nmtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_nmtc_div,
  "HHChar"  = m2_nmtc_div,
  "REM" = m3_nmtc_div,
  "HOUSETRANSPT" = m4_nmtc_div,
  "OVERALL" = m5_nmtc_div,
  "Median Income (USD, logged)" = m6_nmtc_div,
  "Median Home Value (USD, logged)" = m7_nmtc_div,
  "House Price Index (logged)" = m8_nmtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of NMTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))

Differences-in-Differences Linear Regression Analysis of NMTC in South Atlantic Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
(Intercept)	3.08***	1.76***	0.64***	1.44***	6.92***	9.87***	11.43***	4.80***
	(0.18)	(0.13)	(0.06)	(0.13)	(0.36)	(0.04)	(0.05)	(0.08)
treat	0.91***	0.26***	0.18***	0.44***	1.78***	-0.21***	-0.09***	-0.08**
	(0.08)	(0.06)	(0.03)	(0.06)	(0.17)	(0.02)	(0.02)	(0.03)
post	0.01	-0.04	-0.00	0.01	-0.02	-0.00	-0.14***	0.33***
	(0.03)	(0.02)	(0.01)	(0.02)	(0.06)	(0.01)	(0.01)	(0.01)
treat × post	-0.15	-0.13	-0.02	0.00	-0.29	0.06*	0.02	0.05
	(0.12)	(0.08)	(0.04)	(0.09)	(0.23)	(0.03)	(0.03)	(0.04)
Num.Obs.	10490	10490	10490	10490	10490	10482	10248	5086
R2	0.168	0.098	0.247	0.081	0.177	0.201	0.424	0.371
R2 Adj.	0.158	0.087	0.237	0.069	0.166	0.191	0.416	0.356
RMSE	1.39	0.96	0.43	1.00	2.73	0.29	0.39	0.31
p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).

Differences-in-Differences Linear Regression Analysis of NMTC in South Atlantic Division

In the above graph, we can use the different combined results to understand how the NMTC program affected, or did not affect, Social Economic Status (SES). We can see this through:

The starting amount of flags for the column (Intercept)
The effect of the program to the column (Intercept + Treat)
The change after the initial period, regaurdless of whether or not in the program (Intercept + Post)
The change after the initial period for those in the program (Intercept + Treat x Post)

In the South Atlantic Division for those in the NMTC program, we can see that there was a slight decrease in the amount of flags. However, the changes were not statistically significant. For this reason, we are unable to determine whether or not the program was successful in affecting SES.

Due to our results, it is possible that a different model, or including omitted variables, would lead to better results.

Visualize NMTC Divisional Models

Since we did not have any significant significance, we do not need to visualize our outcomes.

LIHTC Divisional

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
lihtc_did10_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(lihtc_did10_div_svi)

## [1] 538

# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
lihtc_did20_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(lihtc_did20_div_svi)

## [1] 538

lihtc_diff_in_diff_div_svi <- bind_rows(lihtc_did10_div_svi, lihtc_did20_div_svi)

lihtc_diff_in_diff_div_svi <- lihtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_svi)

## [1] 1076

Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
lihtc_did10_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(lihtc_did10_div_inc)

## [1] 537

# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
lihtc_did19_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(lihtc_did19_div_inc)

## [1] 537

lihtc_diff_in_diff_div_inc <- bind_rows(lihtc_did10_div_inc, lihtc_did19_div_inc)

lihtc_diff_in_diff_div_inc <- lihtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_inc)

## [1] 1074

Home Value

lihtc_did10_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(lihtc_did10_div_mhv)

## [1] 514

lihtc_did19_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(lihtc_did19_div_mhv)

## [1] 514

lihtc_diff_in_diff_div_mhv <- bind_rows(lihtc_did10_div_mhv, lihtc_did19_div_mhv)

lihtc_diff_in_diff_div_mhv <- lihtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_mhv)

## [1] 1028

House Price Index

lihtc_did10_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(lihtc_did10_div_hpi)

## [1] 140

lihtc_did20_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(lihtc_did20_div_hpi)

## [1] 140

lihtc_diff_in_diff_div_hpi <- bind_rows(lihtc_did10_div_hpi, lihtc_did20_div_hpi)

lihtc_diff_in_diff_div_hpi <- lihtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_hpi)

## [1] 280

LIHTC Divisional Model

# SVI & Economic Models

m1_lihtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m2_lihtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m3_lihtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m4_lihtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m5_lihtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi)

m6_lihtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_inc )

m7_lihtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_mhv )

m8_lihtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_lihtc_div,
  "HHChar"  = m2_lihtc_div,
  "REM" = m3_lihtc_div,
  "HOUSETRANSPT" = m4_lihtc_div,
  "OVERALL" = m5_lihtc_div,
  "Median Income (USD, logged)" = m6_lihtc_div,
  "Median Home Value (USD, logged)" = m7_lihtc_div,
  "House Price Index (logged)" = m8_lihtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of LIHTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))

Differences-in-Differences Linear Regression Analysis of LIHTC in South Atlantic Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
(Intercept)	4.64***	2.54***	1.03***	2.00**	10.21***	9.48***	11.14***	5.61***
	(0.89)	(0.68)	(0.27)	(0.64)	(1.63)	(0.29)	(0.39)	(0.32)
treat	0.23	0.40***	0.11*	0.28**	1.02***	-0.01	-0.02	-0.16
	(0.15)	(0.11)	(0.05)	(0.11)	(0.27)	(0.05)	(0.07)	(0.11)
post	-0.28**	-0.09	-0.06*	0.01	-0.42**	0.08**	-0.11**	0.46***
	(0.09)	(0.07)	(0.03)	(0.06)	(0.16)	(0.03)	(0.04)	(0.06)
treat × post	0.03	-0.12	0.00	-0.02	-0.10	-0.00	0.00	0.09
	(0.20)	(0.15)	(0.06)	(0.15)	(0.37)	(0.06)	(0.09)	(0.14)
Num.Obs.	1006	1006	1006	1006	1006	1004	958	274
R2	0.264	0.243	0.367	0.168	0.278	0.313	0.412	0.398
R2 Adj.	0.203	0.180	0.315	0.100	0.218	0.256	0.360	0.318
RMSE	1.21	0.92	0.37	0.87	2.21	0.39	0.53	0.42
p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).

Differences-in-Differences Linear Regression Analysis of LIHTC in South Atlantic Division

In the above graph, we can use the different combined results to understand how the LIHTC program affected, or did not affect, Social Economic Status (SES). We can see this through:

The starting amount of flags for the column (Intercept)
The effect of the program to the column (Intercept + Treat)
The change after the initial period, regaurdless of whether or not in the program (Intercept + Post)
The change after the initial period for those in the program (Intercept + Treat x Post)

In the South Atlantic Division for those in the LIHTC program, we see a slight increase for SES. However, the change is not statistically significant. For this reason, we are unable to determine whether or not the program was successful in affecting SES.

Due to our results, it is possible that a different model, or including omitted variables, would lead to better results.

Visualize NMTC Divisional Models

Since we once again did not have any significant significance, we do not need to visualize our outcomes.

# Save data sets

saveRDS(svi_divisional_lihtc_df, file = here::here(paste0("data/rodeo/", str_replace_all(census_division, " ", "_"), "_svi_divisional_lihtc.rds")))

saveRDS(svi_national_lihtc_df, file = here::here(paste0("data/rodeo/", str_replace_all(census_division, " ", "_"), "_svi_national_lihtc.rds")))

saveRDS(svi_divisional_nmtc_df, file = here::here(paste0("data/rodeo/", str_replace_all(census_division, " ", "_"), "_svi_divisional_nmtc.rds")))

saveRDS(svi_national_nmtc_df, file = here::here(paste0("data/rodeo/", str_replace_all(census_division, " ", "_"), "_svi_national_nmtc.rds")))

# Save regression models

save(m1_nmtc_div, m2_nmtc_div, m3_nmtc_div, m4_nmtc_div, m5_nmtc_div, m6_nmtc_div, m7_nmtc_div, m8_nmtc_div, file = here::here(paste0("data/rodeo/", str_replace_all(census_division, " ", "_"), "_svi_did_models_nmtc.RData")), compress=TRUE, compression_level = 9)

save(m1_lihtc_div, m2_lihtc_div, m3_lihtc_div, m4_lihtc_div, m5_lihtc_div, m6_lihtc_div, m7_lihtc_div, m8_lihtc_div, file = here::here(paste0("data/rodeo/", str_replace_all(census_division, " ", "_"), "_svi_did_models_lihtc.RData")), compress=TRUE, compression_level = 9)